Intel released the powerful Pentium processor in 1993, establishing a long-running brand of high-performance processors.1 (#fn:lineage) The Pentium includes a floating-point unit that can rapidly compute functions such as sines, cosines, logarithms, and exponentials. But how does the Pentium compute these functions? Earlier Intel chips used binary algorithms called CORDIC, but the Pentium switched to polynomials to approximate these transcendental functions much faster. The polynomials have carefully-optimized coefficients that are stored in a special ROM inside the chip’s floating-point unit. Even though the Pentium is a complex chip with 3.1 million transistors, it is possible to see these transistors under a microscope and read out these constants. The first part of this post discusses how the floating point constant ROM is implemented in hardware. The second part explains how the Pentium uses these constants to evaluate sin, log, and other functions.

The photo below shows the Pentium’s thumbnail-sized silicon die under a microscope. I’ve labeled the main functional blocks; the floating-point unit is in the lower right. The constant ROM (highlighted) is at the bottom of the floating-point unit. Above the floating-point unit, the microcode ROM holds micro-instructions, the individual steps for complex instructions. To execute an instruction such as sine, the microcode ROM directs the floating-point unit through dozens of steps to compute the approximation polynomial using constants from the constant ROM.

Die photo of the Intel Pentium processor with the floating point constant ROM highlighted in red. Click this image (or any other) for a larger version.

Finding pi in the constant ROM In binary, pi is 11.00100100001111110… but what does this mean? To interpret this, the value 11 to the left of the binary point is simply 3 in binary. (The “binary point” is the same as a decimal point, except for binary.) The digits to the right of the binary point have the values 1/2, 1/4, 1/8, and so forth. Thus, the binary value `11.001001000011… corresponds to 3 + 1/8 + 1/64 + 1/4096 + 1/8192 + …, which matches the decimal value of pi. Since pi is irrational, the bit sequence is infinite and non-repeating; the value in the ROM is truncated to 67 bits and stored as a floating point number.

A floating point number is represented by two parts: the exponent and the significand. Floating point numbers include very large numbers such as 6.02×1023 and very small numbers such as 1.055×10−34. In decimal, 6.02×1023 has a significand (or mantissa) of 6.02, multiplied by a power of 10 with an exponent of 23. In binary, a floating point number is represented similarly, with a significand and exponent, except the significand is multiplied by a power of 2 rather than 10. For example, pi is represented in floating point as 1.1001001…×21.

The diagram below shows how pi is encoded in the Pentium chip. Zooming in shows the constant ROM. Zooming in on a small part of the ROM shows the rows of transistors that store the constants. The arrows point to the transistors representing the bit sequence 11001001, where a 0 bit is represented by a transistor (vertical white line) and a 1 bit is represented by no transistor (solid dark silicon). Each magnified black rectangle at the bottom has two potential transistors, storing two bits. The key point is that by looking at the pattern of stripes, we can determine the pattern of transistors and thus the value of each constant, pi in this case.

A portion of the floating-point ROM, showing the value of pi. Click this image (or any other) for a larger version.

The bits are spread out because each row of the ROM holds eight interleaved constants to improve the layout. Above the ROM bits, multiplexer circuitry selects the desired constant from the eight in the activated row. In other words, by selecting a row and then one of the eight constants in the row, one of the 304 constants in the ROM is accessed. The ROM stores many more digits of pi than shown here; the diagram shows 8 of the 67 significand bits.

Implementation of the constant ROM The ROM is built from MOS (metal-oxide-semiconductor) transistors, the transistors used in all modern computers. The diagram below shows the structure of an MOS transistor. An integrated circuit is constructed from a silicon substrate. Regions of the silicon are doped with impurities to create “diffusion” regions with desired electrical properties. The transistor can be viewed as a switch, allowing current to flow between two diffusion regions called the source and drain. The transistor is controlled by the gate, made of a special type of silicon called polysilicon. Applying voltage to the gate lets current flow between the source and drain, which is otherwise blocked. Most computers use two types of MOS transistors: NMOS and PMOS. The two types have similar construction but reverse the doping; NMOS uses n-type diffusion regions as shown below, while PMOS uses p-type diffusion regions. Since the two types are complementary (C), circuits built with the two types of transistors are called CMOS.

Structure of a MOSFET in an integrated circuit.

The image below shows how a transistor in the ROM looks under the microscope. The pinkish regions are the doped silicon that forms the transistor’s source and drain. The vertical white line is the polysilicon that forms the transistor’s gate. For this photo, I removed the chip’s three layers of metal, leaving just the underlying silicon and the polysilicon. The circles in the source and drain are tungsten contacts that connect the silicon to the metal layer above.

One transistor in the constant ROM.

The diagram below shows eight bits of storage. Each of the four pink silicon rectangles has two potential transistors. If a polysilicon gate crosses the silicon, a transistor is formed; otherwise there is no transistor. When a select line (horizontal polysilicon) is energized, it will turn on all the transistors in that row. If a transistor is present, the corresponding ROM bit is 0 because the transistor will pull the output line to ground. If a transistor is absent, the ROM bit is 1. Thus, the pattern of transistors determines the data stored in the ROM. The ROM holds 26144 bits (304 words of 86 bits) so it has 26144 potential transistors.

Eight bits of storage in the ROM.

The photo below shows the bottom layer of metal (M1): vertical metal wires that provide the ROM outputs and supply ground to the ROM. (These wires are represented by gray lines in the schematic above.) The polysilicon transistors (or gaps as appropriate) are barely visible between the metal lines. Most of the small circles are tungsten contacts to the silicon or polysilicon; compare with the photo above. Other circles are tungsten vias to the metal layer on top (M2), horizontal wiring that I removed for this photo. The smaller metal “tabs” act as jumpers between the horizontal metal select lines in M2 and the polysilicon select lines. The top metal layer (M3, not visible) has thicker vertical wiring for the chip’s primary distribution power and ground. Thus, the three metal layers alternate between horizontal and vertical wiring, with vias between the layers.

A closeup of the ROM showing the bottom metal layer.

The ROM is implemented as two grids of cells (below): one to hold exponents and one to hold significands, as shown below. The exponent grid (on the left) has 38 rows and 144 columns of transistors, while the significand grid (on the right) has 38 rows and 544 columns. To make the layout work better, each row holds eight different constants; the bits are interleaved so the ROM holds the first bit of eight constants, then the second bit of eight constants, and so forth. Thus, with 38 rows, the ROM holds 304 constants; each constant has 18 bits in the exponent part and 68 bits in the significand section.

A diagram of the constant ROM and supporting circuitry. Most of the significand ROM has been cut out to make it fit.

The exponent part of each constant consists of 18 bits: a 17-bit exponent and one bit for the sign of the significand and thus the constant. There is no sign bit for the exponent because the exponent is stored with 65535 (0x0ffff) added to it, avoiding negative values. The 68-bit significand entry in the ROM consists of a mysterious flag bit2 (#fn:flag) followed by the 67-bit significand; the first bit of the significand is the integer part and the remainder is the fractional part.3 (#fn:significand) The complete contents of the ROM are in the appendix at the bottom of this post.

To select a particular constant, the “row select” circuitry between the two sections activates one of the 38 rows. That row provides 144+544 bits to the selection circuitry above the ROM. This circuitry has 86 multiplexers; each multiplexer selects one bit out of the group of 8, selecting the desired constant. The significand bits flow into the floating-point unit datapath circuitry above the ROM. The exponent circuitry, however, is in the upper-left corner of the floating-point unit, a considerable distance from the ROM, so the exponent bits travel through a bus to the exponent circuitry.

The row select circuitry consists of gates to decode the row number, along with high-current drivers to energize the selected row in the ROM. The photo below shows a closeup of two row driver circuits, next to some ROM cells. At the left, PMOS and NMOS transistors implement a gate to select the row. Next, larger NMOS and PMOS transistors form part of the driver. The large square structures are bipolar NPN transistors; the Pentium is unusual because it uses both bipolar transistors and CMOS, a technique called BiCMOS.4 (#fn:drivers) Each driver occupies as much height as four rows of the ROM, so there are four drivers arranged horizontally; only one is visible in the photo.

ROM drivers implemented with BiCMOS.

Structure of the floating-point unit The floating-point unit is structured with data flowing vertically through horizontal functional units, as shown below. The functional units—adders, shifters, registers, and comparators—are arranged in rows. This collection of functional units with data flowing through them is called the datapath.5 (#fn:integer)

The datapath of the floating-point unit. The ROM is at the bottom.

Each functional unit is constructed from cells, one per bit, with the high-order bit on the left and the low-order bit on the right. Each cell has the same width—38.5 µm—so the functional units can be connected like Lego blocks snapping together, minimizing the wiring. The height of a functional unit varies as needed, depending on the complexity of the circuit. Functional units typically have 69 bits, but some are wider, so the edges of the datapath circuitry are ragged.

This cell-based construction explains why the ROM has eight constants per row. A ROM bit requires a single transistor, which is much narrower than, say, an adder. Thus, putting one bit in each 38.5 µm cell would waste most of the space. Compacting the ROM bits into a narrow block would also be inefficient, requiring diagonal wiring to connect each ROM bit to the corresponding datapath bit. By putting eight bits for eight different constants into each cell, the width of a ROM cell matches the rest of the datapath and the alignment of bits is preserved. Thus, the layout of the ROM in silicon is dense, efficient, and matches the width of the rest of the floating-point unit.

Polynomial approximation: don’t use a Taylor series Now I’ll move from the hardware to the constants. If you look at the constant ROM contents in the appendix, you may notice that many constants are close to reciprocals or reciprocal factorials, but don’t quite match. For instance, one constant is 0.1111111089, which is close to 1/9, but visibly wrong. Another constant is almost 1/13! (factorial) but wrong by 0.1%. What’s going on?

The Pentium uses polynomials to approximate transcendental functions (sine, cosine, tangent, arctangent, and base-2 powers and logarithms). Intel’s earlier floating-point units, from the 8087 to the 486, used an algorithm called CORDIC that generated results a bit at a time. However, the Pentium takes advantage of its fast multiplier and larger ROM and uses polynomials instead, computing results two to three times faster than the 486 algorithm.

You may recall from calculus that a Taylor series polynomial approximates a function near a point (typically 0). For example, the equation below gives the Taylor series for sine.

Using the five terms shown above generates a function that looks indistinguishable from sine in the graph below. However, it turns out that this approximation has too much error to be useful.

Plot of the sine function and the Taylor series approximation.

The problem is that a Taylor series is very accurate near 0, but the error soars near the edges of the argument range, as shown in the graph on the left below. When implementing a function, we want the function to be accurate everywhere, not just close to 0, so the Taylor series isn’t good enough.

The absolute error for a Taylor-series approximation to sine (5 terms), over two different argument ranges.

One improvement is called range reduction: shrinking the argument to a smaller range so you’re in the accurate flat part.6 (#fn:range) The graph on the right looks at the Taylor series over the smaller range [-1/32, 1/32]. This decreases the error dramatically, by about 22 orders of magnitude (note the scale change). However, the error still shoots up at the edges of the range in exactly the same way. No matter how much you reduce the range, there is almost no error in the middle, but the edges have a lot of error.7 (#fn:scaling)

How can we get rid of the error near the edges? The trick is to tweak the coefficients of the Taylor series in a special way that will increase the error in the middle, but decrease the error at the edges by much more. Since we want to minimize the maximum error across the range (called minimax), this tradeoff is beneficial. Specifically, the coefficients can be optimized by a process called the Remez algorithm.8 (#fn:remez) As shown below, changing the coefficients by less than 1% dramatically improves the accuracy. The optimized function (blue) has much lower error over the full range, so it is a much better approximation than the Taylor series (orange).

Comparison of the absolute error from the Taylor series and a Remez-optimized polynomial, both with maximum term x9. This Remez polynomial is not one from the Pentium.

To summarize, a Taylor series is useful in calculus, but shouldn’t be used to approximate a function. You get a much better approximation by modifying the coefficients very slightly with the Remez algorithm. This explains why the coefficients in the ROM almost, but not quite, match a Taylor series.

Arctan I’ll now look at the Pentium’s constants for different transcendental functions. The constant ROM contains coefficients for two arctan polynomials, one for single precision and one for double precision. These polynomials almost match the Taylor series, but have been modified for accuracy. The ROM also holds the values for arctan(1/32) through arctan(32/32); the range reduction process uses these constants with a trig identity to reduce the argument range to [-1/64, 1/64].9 (#fn:atan) You can see the arctan constants in the Appendix.

The graph below shows the error for the Pentium’s arctan polynomial (blue) versus the Taylor series of the same length (orange). The Pentium’s polynomial is superior due to the Remez optimization. Although the Taylor series polynomial is much flatter in the middle, the error soars near the boundary. The Pentium’s polynomial wiggles more but it maintains a low error across the whole range. The error in the Pentium polynomial blows up outside this range, but that doesn’t matter.

Comparison of the Pentium’s double-precision arctan polynomial to the Taylor series.

Trig functions Sine and cosine each have two polynomial implementations, one with 4 terms in the ROM and one with 6 terms in the ROM. (Note that coefficients of 1 are not stored in the ROM.) The constant table also holds 16 constants such as sin(36/64) and cos(18/64) that are used for argument range reduction.10 (#fn:sin) The Pentium computes tangent by dividing the sine by the cosine. I’m not showing a graph because the Pentium’s error came out worse than the Taylor series, so either I have an error in a coefficient or I’m doing something wrong.

Exponential The Pentium has an instruction to compute a power of two.11 (#fn:exponential) There are two sets of polynomial coefficients for exponential, one with 6 terms in the ROM and one with 11 terms in the ROM. Curiously, the polynomials in the ROM compute ex, not 2x. Thus, the Pentium must scale the argument by ln(2), a constant that is in the ROM. The error graph below shows the advantage of the Pentium’s polynomial over the Taylor series polynomial.

The Pentium’s 6-term exponential polynomial, compared with the Taylor series.

The polynomial handles the narrow argument range [-1/128, 1/128]. Observe that when computing a power of 2 in binary, exponentiating the integer part of the argument is trivial, since it becomes the result’s exponent. Thus, the function only needs to handle the range [1, 2]. For range reduction, the constant ROM holds 64 values of the form 2n/128-1. To reduce the range from [1, 2] to [-1/128, 1/128], the closest n/128 is subtracted from the argument and then the result is multiplied by the corresponding constant in the ROM. The constants are spaced irregularly, presumably for accuracy; some are in steps of 4/128 and others are in steps of 2/128.

Logarithm The Pentium can compute base-2 logarithms.12 (#fn:log) The coefficients define polynomials for the hyperbolic arctan, which is closely related to log. See the comments for details. The ROM also has 64 constants for range reduction: log2(1+n/64) for odd n from 1 to 63. The unusual feature of these constants is that each constant is split into two pieces to increase the bits of accuracy: the top part has 40 bits of accuracy and the bottom part has 67 bits of accuracy, providing a 107-bit constant in total. The extra bits are required because logarithms are hard to compute accurately.

Other constants The x87 floating-point instruction set provides direct access to a handful of constants—0, 1, pi, log2(10), log2(e), log10(2), and loge(2)—so these constants are stored in the ROM. (These logs are useful for changing the base for logs and exponentials.) The ROM holds other constants for internal use by the floating-point unit such as -1, 2, 7/8, 9/8, pi/2, pi/4, and 2log2(e). The ROM also holds bitmasks for extracting part of a word, for instance accessing 4-bit BCD digits in a word. Although I can interpret most of the values, there are a few mysteries such as a mask with the inscrutable value 0x3e8287c. The ROM has 34 unused entries at the end; these entries hold words that include the descriptive hex value 0xbad or perhaps 0xbadfc for “bad float constant”.

How I examined the ROM To analyze the Pentium, I removed the metal and oxide layers with various chemicals (sulfuric acid, phosphoric acid, Whink). (I later discovered that simply sanding the die works surprisingly well.) Next, I took many photos of the ROM with a microscope (https://www.righto.com/2015/12/creating-high-resolution-integrated.html). The feature size of this Pentium is 800 nm, just slightly larger than visible light (380-700 nm). Thus, the die can be examined under an optical microscope, but it is getting close to the limits. To determine the ROM contents, I tediously went through the ROM images, examining each of the 26144 bits and marking each transistor. After figuring out the ROM format, I wrote programs to combine simple functions in many different combinations to determine the mathematical expression such as arctan(19/32) or log2(10). Because the polynomial constants are optimized and my ROM data has bit errors, my program needed checks for inexact matches, both numerically and bitwise. Finally, I had to determine how the constants would be used in algorithms.

Conclusions By examining the Pentium’s floating-point ROM under a microscope, it is possible to extract the 304 constants stored in the ROM. I was able to determine the meaning of most of these constants and deduce some of the floating-point algorithms used by the Pentium. These constants illustrate how polynomials can efficiently compute transcendental functions. Although Taylor series polynomials are well known, they are surprisingly inaccurate and should be avoided. Minor changes to the coefficients through the Remez algorithm, however, yield much better polynomials.

In a previous article (https://www.righto.com/2020/05/extracting-rom-constants-from-8087-math.html), I examined the floating-point constants stored in the 8087 coprocessor. The Pentium has 304 constants in the Pentium, compared to just 42 in the 8087, supporting more efficient algorithms. Moreover, the 8087 was an external floating-point unit, while the Pentium’s floating-point unit is part of the processor. The changes between the 8087 (1980, 65,000 transistors) and the Pentium (1993, 3.1 million transistors) are due to the exponential improvements in transistor count, as described by Moore’s Law.

I plan to write more about the Pentium so follow me on Bluesky (@righto.com (https://bsky.app/profile/righto.com)) or RSS (https://www.righto.com/feeds/posts/default) for updates. (I’m no longer on Twitter.) I’ve also written about the Pentium division bug (https://www.righto.com/2024/12/this-die-photo-of-pentium-shows.html) and the Pentium Navajo rug (https://www.righto.com/2024/08/pentium-navajo-fairchild-shiprock.html). Thanks to CuriousMarc for microscope help. Thanks to lifthrasiir (https://news.ycombinator.com/item?id=42606975) and Alexia for identifying some constants.

Appendix: The constant ROM The table below lists the 304 constants in the Pentium’s floating-point ROM. The first four columns show the values stored in the ROM: the exponent, the sign bit, the flag bit, and the significand. To avoid negative exponents, exponents are stored with the constant 0x0ffff added. For example, the value 0x0fffe represents an exponent of -1, while 0x10000 represents an exponent of 1. The constant’s approximate decimal value is in the “value” column.

Special-purpose values are colored. Specifically, “normal” numbers are in black. Constants with an exponent of all 0’s are in blue, constants with an exponent of all 1’s are in red, constants with an unusually large or small exponent are in green; these appear to be bitmasks rather than numbers. Unused entries are in gray. Inexact constants (due to Remez optimization) are represented with the approximation symbol “≈”.

This information is from my reverse engineering, so there will be a few errors.

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expSFsignificandvaluemeaning

0 00000 0 0 07878787878787878

BCD mask by 4’s

1 00000 0 0 007f807f807f807f8

BCD mask by 8’s

2 00000 0 0 00007fff80007fff8

BCD mask by 16’s

3 00000 0 0 000000007fffffff8

BCD mask by 32’s

4 00000 0 0 78000000000000000

4-bit mask

5 00000 0 0 18000000000000000

2-bit mask

6 00000 0 0 27000000000000000

?

7 00000 0 0 363c0000000000000

?

8 00000 0 0 3e8287c0000000000

?

9 00000 0 0 470de4df820000000

213×1016

10 00000 0 0 5c3bd5191b525a249

2123/1017

11 00000 0 0 00000000000000007

3-bit mask

12 1ffff 1 1 7ffffffffffffffff

all 1’s

13 00000 0 0 0000007ffffffffff

mask for 32-bit float

14 00000 0 0 00000000000003fff

mask for 64-bit float

15 00000 0 0 00000000000000000

all 0’s

16 0ffff 0 0 40000000000000000  1 1

17 10000 0 0 6a4d3c25e68dc57f2  3.3219280949 log2(10)

18 0ffff 0 0 5c551d94ae0bf85de  1.4426950409 log2(e)

19 10000 0 0 6487ed5110b4611a6  3.1415926536 pi

20 0ffff 0 0 6487ed5110b4611a6  1.5707963268 pi/2

21 0fffe 0 0 6487ed5110b4611a6  0.7853981634 pi/4

22 0fffd 0 0 4d104d427de7fbcc5  0.3010299957 log10(2)

23 0fffe 0 0 58b90bfbe8e7bcd5f  0.6931471806 ln(2)

24 1ffff 0 0 40000000000000000

+infinity

25 0bfc0 0 0 40000000000000000

1/4 of smallest 80-bit denormal?

26 1ffff 1 0 60000000000000000

NaN (not a number)

27 0ffff 1 0 40000000000000000 -1 -1

28 10000 0 0 40000000000000000  2 2

29 00000 0 0 00000000000000001

low bit

30 00000 0 0 00000000000000000

all 0’s

31 00001 0 0 00000000000000000

single exponent bit

32 0fffe 0 0 58b90bfbe8e7bcd5e  0.6931471806 ln(2)

33 0fffe 0 0 40000000000000000  0.5 1/2! (exp Taylor series)

34 0fffc 0 0 5555555555555584f  0.1666666667 ≈1/3!

35 0fffa 0 0 555555555397fffd4  0.0416666667 ≈1/4!

36 0fff8 0 0 444444444250ced0c  0.0083333333 ≈1/5!

37 0fff5 0 0 5b05c3dd3901cea50  0.0013888934 ≈1/6!

38 0fff2 0 0 6806988938f4f2318  0.0001984134 ≈1/7!

39 0fffe 0 0 40000000000000000  0.5 1/2! (exp Taylor series)

40 0fffc 0 0 5555555555555558e  0.1666666667 ≈1/3!

41 0fffa 0 0 5555555555555558b  0.0416666667 ≈1/4!

42 0fff8 0 0 444444444443db621  0.0083333333 ≈1/5!

43 0fff5 0 0 5b05b05b05afd42f4  0.0013888889 ≈1/6!

44 0fff2 0 0 68068068163b44194  0.0001984127 ≈1/7!

45 0ffef 0 0 6806806815d1b6d8a  0.0000248016 ≈1/8!

46 0ffec 0 0 5c778d8e0384c73ab  2.755731e-06 ≈1/9!

47 0ffe9 0 0 49f93e0ef41d6086b  2.755731e-07 ≈1/10!

48 0ffe5 0 0 6ba8b65b40f9c0ce8  2.506632e-08 ≈1/11!

49 0ffe2 0 0 47c5b695d0d1289a8  2.088849e-09 ≈1/12!

50 0fffd 0 0 6dfb23c651a2ef221  0.4296133384 266/128-1

51 0fffd 0 0 75feb564267c8bf6f  0.4609177942 270/128-1

52 0fffd 0 0 7e2f336cf4e62105d  0.4929077283 274/128-1

53 0fffe 0 0 4346ccda249764072  0.5255981507 278/128-1

54 0fffe 0 0 478d74c8abb9b15cc  0.5590044002 282/128-1

55 0fffe 0 0 4bec14fef2727c5cf  0.5931421513 286/128-1

56 0fffe 0 0 506333daef2b2594d  0.6280274219 290/128-1

57 0fffe 0 0 54f35aabcfedfa1f6  0.6636765803 294/128-1

58 0fffe 0 0 599d15c278afd7b60  0.7001063537 298/128-1

59 0fffe 0 0 5e60f4825e0e9123e  0.7373338353 2102/128-1

60 0fffe 0 0 633f8972be8a5a511  0.7753764925 2106/128-1

61 0fffe 0 0 68396a503c4bdc688  0.8142521755 2110/128-1

62 0fffe 0 0 6d4f301ed9942b846  0.8539791251 2114/128-1

63 0fffe 0 0 7281773c59ffb139f  0.8945759816 2118/128-1

64 0fffe 0 0 77d0df730ad13bb90  0.9360617935 2122/128-1

65 0fffe 0 0 7d3e0c0cf486c1748  0.9784560264 2126/128-1

66 0fffc 0 0 642e1f899b0626a74  0.1956643920 233/128-1

67 0fffc 0 0 6ad8abf253fe1928c  0.2086843236 235/128-1

68 0fffc 0 0 7195cda0bb0cb0b54  0.2218460330 237/128-1

69 0fffc 0 0 7865b862751c90800  0.2351510639 239/128-1

70 0fffc 0 0 7f48a09590037417f  0.2486009772 241/128-1

71 0fffd 0 0 431f5d950a896dc70  0.2621973504 243/128-1

72 0fffd 0 0 46a41ed1d00577251  0.2759417784 245/128-1

73 0fffd 0 0 4a32af0d7d3de672e  0.2898358734 247/128-1

74 0fffd 0 0 4dcb299fddd0d63b3  0.3038812652 249/128-1

75 0fffd 0 0 516daa2cf6641c113  0.3180796013 251/128-1

76 0fffd 0 0 551a4ca5d920ec52f  0.3324325471 253/128-1

77 0fffd 0 0 58d12d497c7fd252c  0.3469417862 255/128-1

78 0fffd 0 0 5c9268a5946b701c5  0.3616090206 257/128-1

79 0fffd 0 0 605e1b976dc08b077  0.3764359708 259/128-1

80 0fffd 0 0 6434634ccc31fc770  0.3914243758 261/128-1

81 0fffd 0 0 68155d44ca973081c  0.4065759938 263/128-1

82 0fffd 1 0 4cee3bed56eedb76c -0.3005101637 2-66/128-1

83 0fffd 1 0 50c4875296f5bc8b2 -0.3154987885 2-70/128-1

84 0fffd 1 0 5485c64a56c12cc8a -0.3301662380 2-74/128-1

85 0fffd 1 0 58326c4b169aca966 -0.3445193942 2-78/128-1

86 0fffd 1 0 5bcaea51f6197f61f -0.3585649920 2-82/128-1

87 0fffd 1 0 5f4faef0468eb03de -0.3723096215 2-86/128-1

88 0fffd 1 0 62c12658d30048af2 -0.3857597319 2-90/128-1

89 0fffd 1 0 661fba6cdf48059b2 -0.3989216343 2-94/128-1

90 0fffd 1 0 696bd2c8dfe7a5ffb -0.4118015042 2-98/128-1

91 0fffd 1 0 6ca5d4d0ec1916d43 -0.4244053850 2-102/128-1

92 0fffd 1 0 6fce23bceb994e239 -0.4367391907 2-106/128-1

93 0fffd 1 0 72e520a481a4561a5 -0.4488087083 2-110/128-1

94 0fffd 1 0 75eb2a8ab6910265f -0.4606196011 2-114/128-1

95 0fffd 1 0 78e09e696172efefc -0.4721774108 2-118/128-1

96 0fffd 1 0 7bc5d73c5321bfb9e -0.4834875605 2-122/128-1

97 0fffd 1 0 7e9b2e0c43fcf88c8 -0.4945553570 2-126/128-1

98 0fffc 1 0 53c94402c0c863f24 -0.1636449102 2-33/128-1

99 0fffc 1 0 58661eccf4ca790d2 -0.1726541162 2-35/128-1

100 0fffc 1 0 5cf6413b5d2cca73f -0.1815662751 2-37/128-1

101 0fffc 1 0 6179ce61cdcdce7db -0.1903824324 2-39/128-1

102 0fffc 1 0 65f0e8f35f84645cf -0.1991036222 2-41/128-1

103 0fffc 1 0 6a5bb3437adf1164b -0.2077308674 2-43/128-1

104 0fffc 1 0 6eba4f46e003a775a -0.2162651800 2-45/128-1

105 0fffc 1 0 730cde94abb7410d5 -0.2247075612 2-47/128-1

106 0fffc 1 0 775382675996699ad -0.2330590011 2-49/128-1

107 0fffc 1 0 7b8e5b9dc385331ad -0.2413204794 2-51/128-1

108 0fffc 1 0 7fbd8abc1e5ee49f2 -0.2494929652 2-53/128-1

109 0fffd 1 0 41f097f679f66c1db -0.2575774171 2-55/128-1

110 0fffd 1 0 43fcb5810d1604f37 -0.2655747833 2-57/128-1

111 0fffd 1 0 46032dbad3f462152 -0.2734860021 2-59/128-1

112 0fffd 1 0 48041035735be183c -0.2813120013 2-61/128-1

113 0fffd 1 0 49ff6c57a12a08945 -0.2890536989 2-63/128-1

114 0fffd 1 0 555555555555535f0 -0.3333333333 ≈-1/3 (arctan Taylor series)

115 0fffc 0 0 6666666664208b016  0.2 ≈ 1/5

116 0fffc 1 0 492491e0653ac37b8 -0.1428571307 ≈-1/7

117 0fffb 0 0 71b83f4133889b2f0  0.1110544094 ≈ 1/9

118 0fffd 1 0 55555555555555543 -0.3333333333 ≈-1/3 (arctan Taylor series)

119 0fffc 0 0 66666666666616b73  0.2 ≈ 1/5

120 0fffc 1 0 4924924920fca4493 -0.1428571429 ≈-1/7

121 0fffb 0 0 71c71c4be6f662c91  0.1111111089 ≈ 1/9

122 0fffb 1 0 5d16e0bde0b12eee8 -0.0909075848 ≈-1/11

123 0fffb 0 0 4e403be3e3c725aa0  0.0764169081 ≈ 1/13

124 00000 0 0 40000000000000000

single bit mask

125 0fff9 0 0 7ff556eea5d892a14  0.0312398334 arctan(1/32)

126 0fffa 0 0 7fd56edcb3f7a71b6  0.0624188100 arctan(2/32)

127 0fffb 0 0 5fb860980bc43a305  0.0934767812 arctan(3/32)

128 0fffb 0 0 7f56ea6ab0bdb7196  0.1243549945 arctan(4/32)

129 0fffc 0 0 4f5bbba31989b161a  0.1549967419 arctan(5/32)

130 0fffc 0 0 5ee5ed2f396c089a4  0.1853479500 arctan(6/32)

131 0fffc 0 0 6e435d4a498288118  0.2153576997 arctan(7/32)

132 0fffc 0 0 7d6dd7e4b203758ab  0.2449786631 arctan(8/32)

133 0fffd 0 0 462fd68c2fc5e0986  0.2741674511 arctan(9/32)

134 0fffd 0 0 4d89dcdc1faf2f34e  0.3028848684 arctan(10/32)

135 0fffd 0 0 54c2b6654735276d5  0.3310960767 arctan(11/32)

136 0fffd 0 0 5bd86507937bc239c  0.3587706703 arctan(12/32)

137 0fffd 0 0 62c934e5286c95b6d  0.3858826694 arctan(13/32)

138 0fffd 0 0 6993bb0f308ff2db2  0.4124104416 arctan(14/32)

139 0fffd 0 0 7036d3253b27be33e  0.4383365599 arctan(15/32)

140 0fffd 0 0 76b19c1586ed3da2b  0.4636476090 arctan(16/32)

141 0fffd 0 0 7d03742d50505f2e3  0.4883339511 arctan(17/32)

142 0fffe 0 0 4195fa536cc33f152  0.5123894603 arctan(18/32)

143 0fffe 0 0 4495766fef4aa3da8  0.5358112380 arctan(19/32)

144 0fffe 0 0 47802eaf7bfacfcdb  0.5585993153 arctan(20/32)

145 0fffe 0 0 4a563964c238c37b1  0.5807563536 arctan(21/32)

146 0fffe 0 0 4d17c07338deed102  0.6022873461 arctan(22/32)

147 0fffe 0 0 4fc4fee27a5bd0f68  0.6231993299 arctan(23/32)

148 0fffe 0 0 525e3e8c9a7b84921  0.6435011088 arctan(24/32)

149 0fffe 0 0 54e3d5ee24187ae45  0.6632029927 arctan(25/32)

150 0fffe 0 0 5756261c5a6c60401  0.6823165549 arctan(26/32)

151 0fffe 0 0 59b598e48f821b48b  0.7008544079 arctan(27/32)

152 0fffe 0 0 5c029f15e118cf39e  0.7188299996 arctan(28/32)

153 0fffe 0 0 5e3daef574c579407  0.7362574290 arctan(29/32)

154 0fffe 0 0 606742dc562933204  0.7531512810 arctan(30/32)

155 0fffe 0 0 627fd7fd5fc7deaa4  0.7695264804 arctan(31/32)

156 0fffe 0 0 6487ed5110b4611a6  0.7853981634 arctan(32/32)

157 0fffc 1 0 55555555555555555 -0.1666666667 ≈-1/3! (sin Taylor series)

158 0fff8 0 0 44444444444443e35  0.0083333333 ≈ 1/5!

159 0fff2 1 0 6806806806773c774 -0.0001984127 ≈-1/7!

160 0ffec 0 0 5c778e94f50956d70  2.755732e-06 ≈ 1/9!

161 0ffe5 1 0 6b991122efa0532f0 -2.505209e-08 ≈-1/11!

162 0ffde 0 0 58303f02614d5e4d8  1.604139e-10 ≈ 1/13!

163 0fffd 1 0 7fffffffffffffffe -0.5 ≈-1/2! (cos Taylor series)

164 0fffa 0 0 55555555555554277  0.0416666667 ≈ 1/4!

165 0fff5 1 0 5b05b05b05a18a1ba -0.0013888889 ≈-1/6!

166 0ffef 0 0 680680675b559f2cf  0.0000248016 ≈ 1/8!

167 0ffe9 1 0 49f93af61f5349300 -2.755730e-07 ≈-1/10!

168 0ffe2 0 0 47a4f2483514c1af8  2.085124e-09 ≈ 1/12!

169 0fffc 1 0 55555555555555445 -0.1666666667 ≈-1/3! (sin Taylor series)

170 0fff8 0 0 44444444443a3fdb6  0.0083333333 ≈ 1/5!

171 0fff2 1 0 68068060b2044e9ae -0.0001984127 ≈-1/7!

172 0ffec 0 0 5d75716e60f321240  2.785288e-06 ≈ 1/9!

173 0fffd 1 0 7fffffffffffffa28 -0.5 ≈-1/2! (cos Taylor series)

174 0fffa 0 0 555555555539cfae6  0.0416666667 ≈ 1/4!

175 0fff5 1 0 5b05b050f31b2e713 -0.0013888889 ≈-1/6!

176 0ffef 0 0 6803988d56e3bff10  0.0000247989 ≈ 1/8!

177 0fffe 0 0 44434312da70edd92  0.5333026735 sin(36/64)

178 0fffe 0 0 513ace073ce1aac13  0.6346070800 sin(44/64)

179 0fffe 0 0 5cedda037a95df6ee  0.7260086553 sin(52/64)

180 0fffe 0 0 672daa6ef3992b586  0.8060811083 sin(60/64)

181 0fffd 0 0 470df5931ae1d9460  0.2775567516 sin(18/64)

182 0fffd 0 0 5646f27e8bd65cbe4  0.3370200690 sin(22/64)

183 0fffd 0 0 6529afa7d51b12963  0.3951673302 sin(26/64)

184 0fffd 0 0 73a74b8f52947b682  0.4517714715 sin(30/64)

185 0fffe 0 0 6c4741058a93188ef  0.8459244992 cos(36/64)

186 0fffe 0 0 62ec41e9772401864  0.7728350058 cos(44/64)

187 0fffe 0 0 5806149bd58f7d46d  0.6876855622 cos(52/64)

188 0fffe 0 0 4bc044c9908390c72  0.5918050751 cos(60/64)

189 0fffe 0 0 7af8853ddbbe9ffd0  0.9607092430 cos(18/64)

190 0fffe 0 0 7882fd26b35b03d34  0.9414974631 cos(22/64)

191 0fffe 0 0 7594fc1cf900fe89e  0.9186091558 cos(26/64)

192 0fffe 0 0 72316fe3386a10d5a  0.8921336994 cos(30/64)

193 0ffff 0 0 48000000000000000  1.125 9/8

194 0fffe 0 0 70000000000000000  0.875 7/8

195 0ffff 0 0 5c551d94ae0bf85de  1.4426950409 log2(e)

196 10000 0 0 5c551d94ae0bf85de  2.8853900818 2log2(e)

197 0fffb 0 0 7b1c2770e81287c11  0.1202245867 ≈1/(41⋅3⋅ln(2)) (atanh series for log)

198 0fff9 0 0 49ddb14064a5d30bd  0.0180336880 ≈1/(42⋅5⋅ln(2))

199 0fff6 0 0 698879b87934f12e0  0.0032206148 ≈1/(43⋅7⋅ln(2))

200 0fffa 0 0 51ff4ffeb20ed1749  0.0400377512 ≈(ln(2)/2)2/3 (atanh series for log)

201 0fff6 0 0 5e8cd07eb1827434a  0.0028854387 ≈(ln(2)/2)4/5

202 0fff3 0 0 40e54061b26dd6dc2  0.0002475567 ≈(ln(2)/2)6/7

203 0ffef 0 0 61008a69627c92fb9  0.0000231271 ≈(ln(2)/2)8/9

204 0ffec 0 0 4c41e6ced287a2468  2.272648e-06 ≈(ln(2)/2)10/11

205 0ffe8 0 0 7dadd4ea3c3fee620  2.340954e-07 ≈(ln(2)/2)12/13

206 0fff9 0 0 5b9e5a170b8000000  0.0223678130 log2(1+1/64) top bits

207 0fffb 0 0 43ace37e8a8000000  0.0660892054 log2(1+3/64) top bits

208 0fffb 0 0 6f210902b68000000  0.1085244568 log2(1+5/64) top bits

209 0fffc 0 0 4caba789e28000000  0.1497471195 log2(1+7/64) top bits

210 0fffc 0 0 6130af40bc0000000  0.1898245589 log2(1+9/64) top bits

211 0fffc 0 0 7527b930c98000000  0.2288186905 log2(1+11/64) top bits

212 0fffd 0 0 444c1f6b4c0000000  0.2667865407 log2(1+13/64) top bits

213 0fffd 0 0 4dc4933a930000000  0.3037807482 log2(1+15/64) top bits

214 0fffd 0 0 570068e7ef8000000  0.3398500029 log2(1+17/64) top bits

215 0fffd 0 0 6002958c588000000  0.3750394313 log2(1+19/64) top bits

216 0fffd 0 0 68cdd829fd8000000  0.4093909361 log2(1+21/64) top bits

217 0fffd 0 0 7164beb4a58000000  0.4429434958 log2(1+23/64) top bits

218 0fffd 0 0 79c9aa879d8000000  0.4757334310 log2(1+25/64) top bits

219 0fffe 0 0 40ff6a2e5e8000000  0.5077946402 log2(1+27/64) top bits

220 0fffe 0 0 450327ea878000000  0.5391588111 log2(1+29/64) top bits

221 0fffe 0 0 48f107509c8000000  0.5698556083 log2(1+31/64) top bits

222 0fffe 0 0 4cc9f1aad28000000  0.5999128422 log2(1+33/64) top bits

223 0fffe 0 0 508ec1fa618000000  0.6293566201 log2(1+35/64) top bits

224 0fffe 0 0 5440461c228000000  0.6582114828 log2(1+37/64) top bits

225 0fffe 0 0 57df3fd0780000000  0.6865005272 log2(1+39/64) top bits

226 0fffe 0 0 5b6c65a9d88000000  0.7142455177 log2(1+41/64) top bits

227 0fffe 0 0 5ee863e4d40000000  0.7414669864 log2(1+43/64) top bits

228 0fffe 0 0 6253dd2c1b8000000  0.7681843248 log2(1+45/64) top bits

229 0fffe 0 0 65af6b4ab30000000  0.7944158664 log2(1+47/64) top bits

230 0fffe 0 0 68fb9fce388000000  0.8201789624 log2(1+49/64) top bits

231 0fffe 0 0 6c39049af30000000  0.8454900509 log2(1+51/64) top bits

232 0fffe 0 0 6f681c731a0000000  0.8703647196 log2(1+53/64) top bits

233 0fffe 0 0 72896372a50000000  0.8948177633 log2(1+55/64) top bits

234 0fffe 0 0 759d4f80cb8000000  0.9188632373 log2(1+57/64) top bits

235 0fffe 0 0 78a450b8380000000  0.9425145053 log2(1+59/64) top bits

236 0fffe 0 0 7b9ed1c6ce8000000  0.9657842847 log2(1+61/64) top bits

237 0fffe 0 0 7e8d3845df0000000  0.9886846868 log2(1+63/64) top bits

238 0ffd0 1 0 6eb3ac8ec0ef73f7b -1.229037e-14 log2(1+1/64) bottom bits

239 0ffcd 1 0 654c308b454666de9 -1.405787e-15 log2(1+3/64) bottom bits

240 0ffd2 0 0 5dd31d962d3728cbd  4.166652e-14 log2(1+5/64) bottom bits

241 0ffd3 0 0 70d0fa8f9603ad3a6  1.002010e-13 log2(1+7/64) bottom bits

242 0ffd1 0 0 765fba4491dcec753  2.628429e-14 log2(1+9/64) bottom bits

243 0ffd2 1 0 690370b4a9afdc5fb -4.663533e-14 log2(1+11/64) bottom bits

244 0ffd4 0 0 5bae584b82d3cad27  1.628582e-13 log2(1+13/64) bottom bits

245 0ffd4 0 0 6f66cc899b64303f7  1.978889e-13 log2(1+15/64) bottom bits

246 0ffd4 1 0 4bc302ffa76fafcba -1.345799e-13 log2(1+17/64) bottom bits

247 0ffd2 1 0 7579aa293ec16410a -5.216949e-14 log2(1+19/64) bottom bits

248 0ffcf 0 0 509d7c40d7979ec5b  4.475041e-15 log2(1+21/64) bottom bits

249 0ffd3 1 0 4a981811ab5110ccf -6.625289e-14 log2(1+23/64) bottom bits

250 0ffd4 1 0 596f9d730f685c776 -1.588702e-13 log2(1+25/64) bottom bits

251 0ffd4 1 0 680cc6bcb9bfa9853 -1.848298e-13 log2(1+27/64) bottom bits

252 0ffd4 0 0 5439e15a52a31604a  1.496156e-13 log2(1+29/64) bottom bits

253 0ffd4 0 0 7c8080ecc61a98814  2.211599e-13 log2(1+31/64) bottom bits

254 0ffd3 1 0 6b26f28dbf40b7bc0 -9.517022e-14 log2(1+33/64) bottom bits

255 0ffd5 0 0 554b383b0e8a55627  3.030245e-13 log2(1+35/64) bottom bits

256 0ffd5 0 0 47c6ef4a49bc59135  2.550034e-13 log2(1+37/64) bottom bits

257 0ffd5 0 0 4d75c658d602e66b0  2.751934e-13 log2(1+39/64) bottom bits

258 0ffd4 1 0 6b626820f81ca95da -1.907530e-13 log2(1+41/64) bottom bits

259 0ffd3 0 0 5c833d56efe4338fe  8.216774e-14 log2(1+43/64) bottom bits

260 0ffd5 0 0 7c5a0375163ec8d56  4.417857e-13 log2(1+45/64) bottom bits

261 0ffd5 1 0 5050809db75675c90 -2.853343e-13 log2(1+47/64) bottom bits

262 0ffd4 1 0 7e12f8672e55de96c -2.239526e-13 log2(1+49/64) bottom bits

263 0ffd5 0 0 435ebd376a70d849b  2.393466e-13 log2(1+51/64) bottom bits

264 0ffd2 1 0 6492ba487dfb264b3 -4.466345e-14 log2(1+53/64) bottom bits

265 0ffd5 1 0 674e5008e379faa7c -3.670163e-13 log2(1+55/64) bottom bits

266 0ffd5 0 0 5077f1f5f0cc82aab  2.858817e-13 log2(1+57/64) bottom bits

267 0ffd2 0 0 5007eeaa99f8ef14d  3.554090e-14 log2(1+59/64) bottom bits

268 0ffd5 0 0 4a83eb6e0f93f7a64  2.647316e-13 log2(1+61/64) bottom bits

269 0ffd3 0 0 466c525173dae9cf5  6.254831e-14 log2(1+63/64) bottom bits

270 0badf 0 1 40badfc0badfc0bad

unused

271 0badf 0 1 40badfc0badfc0bad

unused

272 0badf 0 1 40badfc0badfc0bad

unused

273 0badf 0 1 40badfc0badfc0bad

unused

274 0badf 0 1 40badfc0badfc0bad

unused

275 0badf 0 1 40badfc0badfc0bad

unused

276 0badf 0 1 40badfc0badfc0bad

unused

277 0badf 0 1 40badfc0badfc0bad

unused

278 0badf 0 1 40badfc0badfc0bad

unused

279 0badf 0 1 40badfc0badfc0bad

unused

280 0badf 0 1 40badfc0badfc0bad

unused

281 0badf 0 1 40badfc0badfc0bad

unused

282 0badf 0 1 40badfc0badfc0bad

unused

283 0badf 0 1 40badfc0badfc0bad

unused

284 0badf 0 1 40badfc0badfc0bad

unused

285 0badf 0 1 40badfc0badfc0bad

unused

286 0badf 0 1 40badfc0badfc0bad

unused

287 0badf 0 1 40badfc0badfc0bad

unused

288 0badf 0 1 40badfc0badfc0bad

unused

289 0badf 0 1 40badfc0badfc0bad

unused

290 0badf 0 1 40badfc0badfc0bad

unused

291 0badf 0 1 40badfc0badfc0bad

unused

292 0badf 0 1 40badfc0badfc0bad

unused

293 0badf 0 1 40badfc0badfc0bad

unused

294 0badf 0 1 40badfc0badfc0bad

unused

295 0badf 0 1 40badfc0badfc0bad

unused

296 0badf 0 1 40badfc0badfc0bad

unused

297 0badf 0 1 40badfc0badfc0bad

unused

298 0badf 0 1 40badfc0badfc0bad

unused

299 0badf 0 1 40badfc0badfc0bad

unused

300 0badf 0 1 40badfc0badfc0bad

unused

301 0badf 0 1 40badfc0badfc0bad

unused

302 0badf 0 1 40badfc0badfc0bad

unused

303 0badf 0 1 40badfc0badfc0bad

unused

Notes and references

• In this blog post, I’m looking at the “P5” version of the original Pentium processor. It can be hard to keep all the Pentiums straight since “Pentium” became a brand name with multiple microarchitectures, lines, and products. The original Pentium (1993) was followed by the Pentium Pro (1995), Pentium II (1997), and so on.

The original Pentium used the P5 microarchitecture, a superscalar microarchitecture that was advanced but still executed instruction in order like traditional microprocessors. The original Pentium went through several substantial revisions. The first Pentium product was the 80501 (codenamed P5), containing 3.1 million transistors. The power consumption of these chips was disappointing, so Intel improved the chip, producing the 80502, codenamed P54C. The P5 and P54C look almost the same on the die, but the P54C added circuitry for multiprocessing, boosting the transistor count to 3.3 million. The biggest change to the original Pentium was the Pentium MMX, with part number 80503 and codename P55C. The Pentium MMX added 57 vector processing instructions and had 4.5 million transistors. The floating-point unit was rearranged in the MMX, but the constants are probably the same. ↩ (#fnref:lineage)

• I don’t know what the flag bit in the ROM indicates; I’m arbitrarily calling it a flag. My wild guess is that it indicates ROM entries that should be excluded from the checksum when testing the ROM. ↩ (#fnref:flag)

• Internally, the significand has one integer bit and the remainder is the fraction, so the binary point (decimal point) is after the first bit. However, this is not the only way to represent the significand. The x87 80-bit floating-point format (double extended-precision) uses the same approach. However, the 32-bit (single-precision) and 64-bit (double-precision) formats drop the first bit and use an “implied” one bit. This gives you one more bit of significand “for free” since in normal cases the first significand bit will be 1. ↩ (#fnref:significand)

• An unusual feature of the Pentium is that it uses bipolar NPN transistors along with CMOS circuits, a technology called BiCMOS. By adding a few extra processing steps to the regular CMOS manufacturing process, bipolar transistors could be created. The Pentium uses BiCMOS circuits extensively since they reduced signal delays by up to 35%. Intel also used BiCMOS for the Pentium Pro, Pentium II, Pentium III, and Xeon processors (but not the Pentium MMX). However, as chip voltages dropped, the benefit from bipolar transistors dropped too and BiCMOS was eventually abandoned.

In the constant ROM, BiCMOS circuits improve the performance of the row selection circuitry. Each row select line is very long and is connected to hundreds of transistors, so the capacitive load is large. Because of the fast and powerful NPN transistor, a BiCMOS driver provides lower delay for higher loads than a regular CMOS driver.

A typical BiCMOS inverter. From A 3.3V 0.6µm BiCMOS superscalar microprocessor (https://doi.org/10.1109/ISSCC.1994.344670).

This BiCMOS logic is also called BiNMOS or BinMOS because the output has a bipolar transistor and an NMOS transistor. For more on BiCMOS circuits in the Pentium, see my article Standard cells: Looking at individual gates in the Pentium processor (https://www.righto.com/2024/07/pentium-standard-cells.html). ↩ (#fnref:drivers)

• The integer processing unit of the Pentium is constructed similarly, with horizontal functional units stacked to form the datapath. Each cell in the integer unit is much wider than a floating-point cell (64 µm vs 38.5 µm). However, the integer unit is just 32 bits wide, compared to 69 (more or less) for the floating-point unit, so the floating-point unit is wider overall. ↩ (#fnref:integer)

• I don’t like referring to the argument’s range since a function’s output is the range, while its input is the domain. But the term range reduction (https://en.wikipedia.org/wiki/Math_library#Trignometry) is what people use, so I’ll go with it. ↩ (#fnref:range)

• There’s a reason why the error curve looks similar even if you reduce the range. The error from the Taylor series is approximately the next term in the Taylor series, so in this case the error is roughly -x11/11! or O(x11). This shows why range reduction is so powerful: if you reduce the range by a factor of 2, you reduce the error by the enormous factor of 211. But this also shows why the error curve keeps its shape: the curve is still x11, just with different labels on the axes. ↩ (#fnref:scaling)

• The Pentium coefficients are probably obtained using the Remez algorithm; see Floating-Point Verification (https://pdfs.semanticscholar.org/6af4/5bef6d5aeb01c532a50872f484e11c7ddc29.pdf). The advantages of the Remez polynomial over the Taylor series are discussed in Better Function Approximations: Taylor vs. Remez (https://web.archive.org/web/20130821201935/http://lolengine.net/blog/2011/12/21/better-function-approximations). A description of Remez’s algorithm is in Elementary Functions: Algorithms and Implementation (https://archive.org/details/elementaryfuncti0000mull/page/41/mode/1up), which has other relevant information on polynomial approximation and range reduction. For more on polynomial approximations, see Numerically Computing the Exponential Function with Polynomial Approximations (https://justinwillmert.com/articles/2020/numerically-computing-the-exponential-function-with-polynomial-approximations/) and The Eight Useful Polynomial Approximations of Sinf(3) (https://pvk.ca/Blog/2012/10/07/the-eight-useful-polynomial-approximations-of-sinf-3/),

The Remez polynomial in the sine graph is not the Pentium polynomial; it was generated for illustration by lolremez (https://github.com/samhocevar/lolremez), a useful tool. The specific polynomial is:

9.9997938808335731e-1 ⋅ x - 1.6662438518867169e-1 ⋅ x3 + 8.3089850302282266e-3 ⋅ x5 - 1.9264997445395096e-4 ⋅ x7 + 2.1478735041839789e-6 ⋅ x9

The graph below shows the error for this polynomial. Note that the error oscillates between an upper bound and a lower bound. This is the typical appearance of a Remez polynomial. In contrast, a Taylor series will have almost no error in the middle and shoot up at the edges. This Remez polynomial was optimized for the range [-π,π]; the error explodes outside that range. The key point is that the Remez polynomial distributes the error inside the range. This minimizes the maximum error (minimax).

↩ (#fnref:remez)Error from a Remez-optimized polynomial for sine.

• I think the arctan argument is range-reduced to the range [-1/64, 1/64]. This can be accomplished with the trig identity arctan(x) = arctan((x-c)/(1+xc)) + arctan(c). The idea is that c is selected to be the value of the form n/32 closest to x. As a result, x-c will be in the desired range and the first arctan can be computed with the polynomial. The other term, arctan(c), is obtained from the lookup table in the ROM. The FPATAN (partial arctangent) instruction takes two arguments, x and y, and returns atan(y/x); this simplifies handling planar coordinates. In this case, the trig identity becomes arcan(y/x) = arctan((y-tx)/(x+ty)) + arctan c. The division operation can trigger the FDIV bug in some cases; see Computational Aspects of the Pentium Affair (https://people.cs.vt.edu/~naren/Courses/CS3414/assignments/pentium.pdf). ↩ (#fnref:atan)

• The Pentium has several trig instructions: FSIN, FCOS, and FSINCOS return the sine, cosine, or both (which is almost as fast as computing either). FPTAN returns the “partial tangent” consisting of two numbers that must be divided to yield the tangent. (This was due to limitations in the original 8087 coprocessor.) The Pentium returns the tangent as the first number and the constant 1 as the second number, keeping the semantics of FPTAN while being more convenient.

The range reduction is probably based on the trig identity sin(a+b) = sin(a)cos(b)+cos(a)sin(b). To compute sin(x), select b as the closest constant in the lookup table, n/64, and then generate a=x-b. The value a will be range-reduced, so sin(a) can be computed from the polynomial. The terms sin(b) and cos(b) are available from the lookup table. The desired value sin(x) can then be computed with multiplications and addition by using the trig identity. Cosine can be computed similarly. Note that cos(a+b) =cos(a)cos(b)-sin(a)sin(b); the terms on the right are the same as for sin(a+b), just combined differently. Thus, once the terms on the right have been computed, they can be combined to generate sine, cosine, or both. The Pentium computes the tangent by dividing the sine by the cosine. This can trigger the FDIV division bug; see Computational Aspects of the Pentium Affair (https://people.cs.vt.edu/~naren/Courses/CS3414/assignments/pentium.pdf).

Also see Agner Fog’s Instruction Timings (https://www.agner.org/optimize/instruction_tables.pdf#page=164); the timings for the various operations give clues as to how they are computed. For instance, FPTAN takes longer than FSINCOS because the tangent is generated by dividing the sine by the cosine. ↩ (#fnref:sin)

• For exponentials, the F2XM1 instruction computes 2x-1; subtracting 1 improves accuracy. Specifically, 2x is close to 1 for the common case when x is close to 0, so subtracting 1 as a separate operation causes you to lose most of the bits of accuracy due to cancellation. On the other hand, if you want 2x, explicitly adding 1 doesn’t harm accuracy. This is an example of how the floating-point instructions are carefully designed to preserve accuracy. For details, see the book The 8087 Primer by the architects of the 8086 processor and the 8087 coprocessor. ↩ (#fnref:exponential)

• The Pentium has base-two logarithm instructions FYL2X and FYL2XP1. The FYL2X instruction computes y log2(x) and the FYL2XP1 instruction computes y log2(x+1) The instructions include a multiplication because most logarithm operations will need to multiply to change the base; performing the multiply with internal precision increases the accuracy. The “plus-one” instruction improves accuracy for arguments close to 1, such as interest calculations.

My hypothesis for range reduction is that the input argument is scaled to fall between 1 and 2. (Taking the log of the exponent part of the argument is trivial since the base-2 log of a base-2 power is simply the exponent.) The argument can then be divided by the largest constant 1+n/64 less than the argument. This will reduce the argument to the range [1, 1+1/32]. The log polynomial can be evaluated on the reduced argument. Finally, the ROM constant for log2(1+n/64) is added to counteract the division. The constant is split into two parts for greater accuracy.

It took me a long time to figure out the log constants because they were split. The upper-part constants appeared to be pointlessly inaccurate since the bottom 27 bits are zeroed out. The lower-part constants appeared to be miniscule semi-random numbers around ±10-13. Eventually, I figured out that the trick was to combine the constants. ↩ (#fnref:log)