Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the “bible of computer algebra”, gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany oneor two-semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, cryptography, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text. In this third edition, errors have been corrected and much of the Fast Euclidean Algorithm chapter has been renovated.

Modern computer algebra

The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

Computational Intelligence: An Introduction

In this paper, we present a new strategy for pricing average value options, i.e. options whose payoff depends on the average price of the underlying asset over a fixed period leading up to the maturity date. Such options are of particular interest and importance for thinly-traded assets (e.g. crude oil), since price manipulation is inhibited, and both the investor and issuer enjoy a welcome degree of protection from the vagaries of the market. These options are often implicit in a bond contract, although they also appear in a straightforward form. Our results suggest that the price of an average-value option will always be lower than that of a standard European option. Our pricing strategy involves Monte Carlo simulation with variance reduction elements and offers an enhanced pricing method to both arbitragers and hedgers, as well as to the issuers of such bonds.

A Pricing Method for Options Based on Average Asset Values

Chip formation during dry turning of Ti6Al4V alloy has been examined in association with dynamic cutting force measurements under different cutting speeds, feed rates and depths of cut. Both continuous and segmented chip formation processes were observed in one cut under conditions of low cutting speed and large feed rate. The slipping angle in the segmented chip was 55°, which was higher than that in the continuous chip (38°). A cyclic force was produced during the formation of segmented chips and the force frequency was the same as the chip segmentation frequency. The peak of the cyclic force when producing segmented chips was 1.18 times that producing the continuous chip. The undeformed surface length in the segmented chip was found to increase linearly with the feed rate but was independent of cutting speed and depth of cut. The cyclic force frequency increased linearly with cutting speed and decreased inversely with feed rate. The cutting force increased with the feed rate and depth of cut at constant cutting speed due to the large volume of material being removed. The increase in cutting force with increasing cutting speed from 10 to 16 and 57 to 75 m/min was attributed to the strain rate hardening at low and high strain rates, respectively. The decrease in cutting force with increasing cutting speed outside these speed ranges was due to the thermal softening of the material. The amplitude variation of the high-frequency cyclic force associated with the segmented chip formation increased with increasing depth of cut and feed rate, and decreased with increasing cutting speed from 57 m/min except at the cutting speeds where harmonic vibration of the machine occurs.

Characteristics of cutting forces and chip formation in machining of titanium alloys

Logic locking is an Intellectual Property (IP) protection technique that thwarts IP piracy, hardware Trojans, reverse engineering, and IC overproduction. Researchers have taken multiple attempts in breaking logic locking techniques and recovering its secret key. A Boolean Satisfiability (SAT) based attack has been recently presented that breaks all the existing combinational logic locking techniques. In this paper, we develop a lightweight countermeasure against this and other attacks that aim at gradually pruning the key search space. Our proposed logic locking technique, referred to as SARLock, maximizes the required number of distinguishing input patterns to recover the secret key. SARLock thwarts the SAT attack by rendering the attack effort exponential in the number of bits in the secret key, while its overhead grows only linearly.

SARLock: SAT attack resistant logic locking

Preface. About the Authors. 1. Introduction. 1.1 Number Representation. 1.2 Algorithms. 1.3 Hardware Platforms. 1.4 Hardware-Software Partitioning. 1.5 Software Generation. 1.6 Synthesis. 1.7 A First Example. 1.7.1 Specification. 1.7.2 Number Representation. 1.7.3 Algorithms. 1.7.4 Hardware Platform. 1.7.5 Hardware-Software Partitioning. 1.7.6 Program Generation. 1.7.7 Synthesis. 1.7.8 Prototype. 1.8 Bibliography. 2. Mathematical Background. 2.1 Number Theory. 2.1.1 Basic Definitions. 2.1.2 Euclidean Algorithms. 2.1.3 Congruences. 2.2 Algebra. 2.2.1 Groups. 2.2.2 Rings. 2.2.3 Fields. 2.2.4 Polynomial Rings. 2.2.5 Congruences of Polynomial. 2.3 Function Approximation. 2.4 Bibliography. 3. Number Representation. 3.1 Natural Numbers. 3.1.1 Weighted Systems. 3.1.2 Residue Number System. 3.2 Integers. 3.2.1 Sign-Magnitude Representation. 3.2.2 Excess-E Representation. 3.2.3 B's Complement Representation. 3.2.4 Booth's Encoding. 3.3 Real Numbers. 3.4 Bibliography. 4. Arithmetic Operations: Addition and Subtraction. 4.1 Addition of Natural Numbers. 4.1.1 Basic Algorithm. 4.1.2 Faster Algorithms. 4.1.3 Long-Operand Addition. 4.1.4 Multioperand Addition. 4.1.5 Long-Multioperand Addition. 4.2 Subtraction of Natural Numbers. 4.3 Integers. 4.3.1 B's Complement Addition. 4.3.2 B's Complement Sign Change. 4.3.3 B's Complement Subtraction. 4.3.4 B's Complement Overflow Detection. 4.3.5 Excess-E Addition and Subtraction. 4.3.6 Sign-Magnitude Addition and Subtraction. 4.4 Bibliography. 5. Arithmetic Operations: Multiplication. 5.1 Natural Numbers Multiplication. 5.1.1 Introduction. 5.1.2 Shift and Add Algorithms. 5.1.2.1 Shift and Add 1. 5.1.2.2 Shift and Add 2. 5.1.2.3 Extended Shift and Add Algorithm: XY t C t D. 5.1.2.4 Cellular Shift and Add. 5.1.3 Long-Operand Algorithm. 5.2 Integers. 5.2.1 B's Complement Multiplication. 5.2.1.1 Mod Bntm B's Complement Multiplication. 5.2.1.2 Signed Shift and Add. 5.2.1.3 Postcorrection B's Complement Multiplication. 5.2.2 Postcorrection 2's Complement Multiplication. 5.2.3 Booth Multiplication for Binary Numbers. 5.2.3.1 Booth-r Algorithms. 5.2.3.2 Per Gelosia Signed-Digit Algorithm. 5.2.4 Booth Multiplication for Base-B Numbers (Booth-r Algorithm in Base B). 5.3 Squaring. 5.3.1 Base-B Squaring. 5.3.1.1 Cellular Carry-Save Squaring Algorithm. 5.3.2 Base-2 Squaring. 5.4 Bibliography. 6 Arithmetic Operations: Division. 6.1 Natural Numbers. 6.2 Integers. 6.2.1 General Algorithm. 6.2.2 Restoring Division Algorithm. 6.2.3 Base-2 Nonrestoring Division Algorithm. 6.2.4 SRT Radix-2 Division. 6.2.5 SRT Radix-2 Division with Stored-Carry Encoding. 6.2.6 P-D Diagram. 6.2.7 SRT-4 Division. 6.2.8 Base-B Nonrestoring Division Algorithm. 6.3 Convergence (Functional Iteration) Algorithms. 6.3.1 Introduction. 6.3.2 Newton-Raphson Iteration Technique. 6.3.3 MacLaurin Expansion-Goldschmidt's Algorithm. 6.4 Bibliography. 7. Other Arithmetic Operations. 7.1 Base Conversion. 7.2 Residue Number System Conversion. 7.2.1 Introduction. 7.2.2 Base-B to RNS Conversion. 7.2.3 RNS to Base-B Conversion. 7.3 Logarithmic, Exponential, and Trigonometric Functions. 7.3.1 Taylor-MacLaurin Series. 7.3.2 Polynomial Approximation. 7.3.3 Logarithm and Exponential Functions Approximation by Convergence Methods. 7.3.3.1 Logarithm Function Approximation by Multiplicative Normalization. 7.3.3.2 Exponential Function Approximation by Additive Normalization. 7.3.4 Trigonometric Functions-CORDIC Algorithms. 7.4 Square Rooting. 7.4.1 Digit Recurrence Algorithm-Base-B Integers. 7.4.2 Restoring Binary Shift-and-Subtract Square Rooting Algorithm. 7.4.3 Nonrestoring Binary Add-and-Subtract Square Rooting Algorithm. 7.4.4 Convergence Method-Newton-Raphson. 7.5 Bibliography. 8. Finite Field Operations. 8.1 Operations in Zm. 8.1.1 Addition. 8.1.2 Subtraction. 8.1.3 Multiplication. 8.1.3.1 Multiply and Reduce. 8.1.3.2 Modified Shift-and-Add Algorithm. 8.1.3.3 Montgomery Multiplication. 8.1.3.4 Specific Ring. 8.1.4 Exponentiation. 8.2 Operations in GF(p). 8.3 Operations in Zp[x]/f (x). 8.3.1 Addition and Subtraction. 8.3.2 Multiplication. 8.4 Operations in GF(pn). 8.5 Bibliography. Appendix 8.1 Computation of fki. 9 Hardware Platforms. 9.1 Design Methods for Electronic Systems. 9.1.1 Basic Blocks of Integrated Systems. 9.1.2 Recurring Topics in Electronic Design. 9.1.2.1 Design Challenge: Optimizing Design Metrics. 9.1.2.2 Cost in Integrated Circuits. 9.1.2.3 Moore's Law. 9.1.2.4 Time-to-Market. 9.1.2.5 Performance Metric. 9.1.2.6 The Power Dimension. 9.2 Instruction Set Processors. 9.2.1 Microprocessors. 9.2.2 Microcontrollers. 9.2.3 Embedded Processors Everywhere. 9.2.4 Digital Signal Processors. 9.2.5 Application-Specific Instruction Set Processors. 9.2.6 Programming Instruction Set Processors. 9.3 ASIC Designs. 9.3.1 Full-Custom ASIC. 9.3.2 Semicustom ASIC. 9.3.2.1 Gate-Array ASIC. 9.3.2.2 Standard-Cell-Based ASIC. 9.3.3 Design Flow in ASIC. 9.4 Programmable Logic. 9.4.1 Programmable Logic Devices (PLDs). 9.4.2 Field Programmable Gate Array (FPGA). 9.4.2.1 Why FPGA? A Short Historical Survey. 9.4.2.2 Basic FPGA Concepts. 9.4.3 XilinxTM Specifics. 9.4.3.1 Configurable Logic Blocks (CLBs). 9.4.3.2 Input/Output Blocks (IOBs). 9.4.3.3 RAM Blocks. 9.4.3.4 Programmable Routing. 9.4.3.5 Arithmetic Resources in Xilinx FPGAs. 9.4.4 FPGA Generic Design Flow. 9.5 Hardware Description Languages (HDLs). 9.5.1 Today's and Tomorrow's HDLs. 9.6 Further Readings. 9.7 Bibliography. 10. Circuit Synthesis: General Principles. 10.1 Resources. 10.2 Precedence Relation and Scheduling. 10.3 Pipeline. 10.4 Self-Timed Circuits. 10.5 Bibliography. 11 Adders and Subtractors. 11.1 Natural Numbers. 11.1.1 Basic Adder (Ripple-Carry Adder). 11.1.2 Carry-Chain Adder. 11.1.3 Carry-Skip Adder. 11.1.4 Optimization of Carry-Skip Adders. 11.1.5 Base-Bs Adder. 11.1.6 Carry-Select Adder. 11.1.7 Optimization of Carry-Select Adders. 11.1.8 Carry-Lookahead Adders (CLAs). 11.1.9 Prefix Adders. 11.1.10 FPGA Implementation of Adders. 11.1.10.1 Carry-Chain Adders. 11.1.10.2 Carry-Skip Adders. 11.1.10.3 Experimental Results. 11.1.11 Long-Operand Adders. 11.1.12 Multioperand Adders. 11.1.12.1 Sequential Multioperand Adders. 11.1.12.2 Combinational Multioperand Adders. 11.1.12.3 Carry-Save Adders. 11.1.12.4 Parallel Counters. 11.1.13 Subtractors and Adder-Subtractors. 11.1.14 Termination Detection. 11.1.15 FPGA Implementation of the Termination Detection. 11.2 Integers. 11.2.1 B's Complement Adders and Subtractors. 11.2.2 Excess-E Adders and Subtractors. 11.2.3 Sign-Magnitude Adders and Subtractors. 11.3 Bibliography. 12 Multipliers. 12.1 Natural Numbers. 12.1.1 Basic Multiplier. 12.1.2 Sequential Multipliers. 12.1.3 Cellular Multiplier Arrays. 12.1.3.1 Ripple-Carry Multiplier. 12.1.3.2 Carry-Save Multiplier. 12.1.3.3 Figures of Merit. 12.1.4 Multipliers Based on Dissymmetric Br Bs Cells. 12.1.5 Multipliers Based on Multioperand Adders. 12.1.6 Per Gelosia Multiplication Arrays. 12.1.6.1 Introduction. 12.1.6.2 Adding Tree for Base-B Partial Products. 12.1.7 FPGA Implementation of Multipliers. 12.2 Integers. 12.2.1 B's Complement Multipliers. 12.2.2 Booth Multipliers. 12.2.2.1 Booth-1 Multiplier. 12.2.2.2 Booth-2 Multiplier. 12.2.2.3 Signed-Digit Multiplier. 12.2.3 FPGA Implementation of the Booth-1 Multiplier. 12.3 Bibliography. 13. Dividers. 13.1 Natural Numbers. 13.2 Integers. 13.2.1 Base-2 Nonrestoring Divider. 13.2.2 Base-B Nonrestoring Divider. 13.2.3 SRT Dividers. 13.2.3.1 SRT-2 Divider. 13.2.3.2 SRT-2 Divider with Carry-Save Computation of the Remainder. 13.2.3.3 FPGA Implementation of the Carry-Save SRT-2 Divider. 13.2.4 SRT-4 Divider. 13.2.5 Convergence Dividers. 13.2.5.1 Newton-Raphson Divider. 13.2.5.2 Goldschmidt Divider. 13.2.5.3 Comparative Data Between Newton-Raphson (NR) and Goldschmidt (G) Implementations. 13.3 Bibliography. 14 Other Arithmetic Operators. 14.1 Base Conversion. 14.1.1 General Base Conversion. 14.1.2 BCD to Binary Converter. 14.1.2.1 Nonrestoring 2p Subtracting Implementation. 14.1.2.2 Shift-and-Add BCD to Binary Converter. 14.1.3 Binary to BCD Converter. 14.1.4 Base-B to RNS Converter. 14.1.5 CRT RNS to Base-B Converter. 14.1.6 RNS to Mixed-Radix System Converter. 14.2 Polynomial Computation Circuits. 14.3 Logarithm Operator. 14.4 Exponential Operator. 14.5 Sine and Cosine Operators. 14.6 Square Rooters. 14.6.1 Restoring Shift-and-Subtract Square Rooter (Naturals). 14.6.2 Nonrestoring Shift-and-Subtract Square Rooter (Naturals). 14.6.3 Newton-Raphson Square Rooter (Naturals). 14.7 Bibliography. 15. Circuits for Finite Field Operations. 15.1 Operations in Zm. 15.1.1 Adders and Subtractors. 15.1.2 Multiplication. 15.1.2.1 Multiply and Reduce. 15.1.2.2 Shift and Add. 15.1.2.3 Montgomery Multiplication. 15.1.2.4 Modulo (Bk2c) Reduction. 15.1.2.5 Exponentiation. 15.2 Inversion in GF(p). 15.3 Operations in Zp[x]/f (x). 15.4 Inversion in GF(pn). 15.5 Bibliography. 16. Floating-Point Unit. 16.1 Floating-Point System Definition. 16.2 Arithmetic Operations. 16.2.1 Addition of Positive Numbers. 16.2.2 Difference of Positive Numbers. 16.2.3 Addition and Subtraction. 16.2.4 Multiplication. 16.2.5 Division. 16.2.6 Square Root. 16.3 Rounding Schemes. 16.4 Guard Digits. 16.5 Adder-Subtractor. 16.5.1 Alignment. 16.5.2 Additions. 16.5.3 Normalization. 16.5.4 Rounding. 16.6 Multiplier. 16.7 Divider. 16.8 Square Root. 16.9 Comments. 16.10 Bibliography. Index.

Synthesis of Arithmetic Circuits: FPGA, ASIC and Embedded Systems

This paper details the design of a new high-speed point multiplier for elliptic curve cryptography using either field-programmable gate array or application-specified integrated circuit technology. Different levels of digit-serial computation were applied to the data path of Galois field (GF) multiplication and division to explore the resulting performances and find out an optimal digit size. We provide results for the five National Institute of Standards and Technology recommended curves, outperforming the previous published results. In GF(2163), we achieve a point multiplication in 19.38 μs in Xilinx Virtex-E. Using the modern Xilinx Virtex-5, the point multiplication times in GF(2m) for m = 163, 233, 409, and 571 are 5.5, 17.8, 33.6, 102.6, 384μs, respectively, which are the fastest figures reported to date.

Efficient Elliptic Curve Point Multiplication Using Digit-Serial Binary Field Operations

Modular exponentiation with large modulus and exponent, which is usually accomplished by repeated modular multiplications, has been widely used in public key cryptosystems. Typically, the Montgomery's modular-multiplication algorithm is used since no trial division is necessary, and the carry-save addition (CSA) is employed to reduce the critical path. In this paper, we optimize the Montgomery's multiplication and propose architectures to perform the least significant bit first and the most significant bit first algorithms. The developed architecture has the following distinctive characteristics: 1) use of digit serial approach for Montgomery multiplication. 2) Conversion of the CSA representation of intermediate multiplication using carry-skip addition. This allows the critical path to be reduced, albeit with a small-area speed penalty; and 3) precompute the quotient value in Montgomery's iteration in order to speed up the operating frequency. In this paper, we present results in Xilinx Virtex 5 and in 0.18-μm application-specified integrated circuit technologies. For fair comparison with previous works, Xilinx Virtex 2 results are reported. Experimental results show that the proposed modular exponentiation and modular-multiplication design obtain the best delay performance compared with the published works and outperform them in terms of area-time complexity as well.

Modular Multiplication and Exponentiation Architectures for Fast RSA Cryptosystem Based on Digit Serial Computation

An original experimental device is used to reproduce conditions of orthogonal cutting for a wide range of cutting speeds (from 15 to about 100 m/s) (Sutter et al.). Improvement of the initial device (Sutter et al.) makes it possible to record both values of normal and tangential forces in orthogonal cutting. An analysis of the tool-chip friction is then possible for a large range of cutting speeds. The evolution of cutting force components as well as the evolution of the friction coefficient are presented and analyzed. In addition, the process of chip formation during high speed machining is illustrated by photographic recording with a high speed camera.

Analysis of the Cutting Force Components and Friction in High Speed Machining

The realization that the network is becoming an important bottleneck in computing clusters and in the cloud has led in the past years to an increase scrutiny of how networking functionality is deployed. From TCP Offload Engines (TOEs) to Software Defined Networking (SDN), including Smart NICs and In-Network Data Processing, a wide range of approaches are currently being explored to increase the efficiency of networks and tailor its functionality to the actual needs of the application at hand. To address the need for an open and customizable networking stack, in this paper we introduce Limago, an FPGA-based open-source implementation of a TCP/IP stack operating at 100 Gbit/s. To our knowledge, Limago provides the first complete description of an FPGA-based TCP/IP stack at these speeds, thereby illustrating the bottlenecks that must be addressed, proposing several innovative designs to reach the necessary throughput, and showing how to incorporate advanced protocol features into the design. As an example, Limago supports the TCP Window Scale option, addressing the Long Fat Pipe issue. Limago not only enables 100 Gbit/s Ethernet links in an open source package, but also paves the way to programmable and fully customizable NICs based on FPGAs.

Gustavo Sutter

Papers

Synthesis of Arithmetic Circuits: FPGA, ASIC and Embedded Systems

Efficient Elliptic Curve Point Multiplication Using Digit-Serial Binary Field Operations

Modular Multiplication and Exponentiation Architectures for Fast RSA Cryptosystem Based on Digit Serial Computation

Analysis of the Cutting Force Components and Friction in High Speed Machining

Limago: An FPGA-Based Open-Source 100 GbE TCP/IP Stack