Design of Unsigned Approximate Hybrid Dividers based on Restoring Array and Logarithmic Dividers

Approximate computer arithmetic has been extensively studied due to its advantages to further reduce power consumption and increase performance at reduced accuracy. Although a number of approximate adders and multipliers have been studied, only a few approximate dividers have been proposed. A logarithmic divider (LD) has low complexity and accuracy, while an exact array divider (EXD) has a high complexity. Therefore, in this paper, an approximate hybrid divider (AXHD) is proposed. It takes advantage of both LD and EXD to achieve a tradeoff between hardware performance and accuracy. Exact restoring divider cells are used to generate the most significant bits (MSBs) of the quotient for attaining a high accuracy while the other quotient digits are generated by using a LD as an approximate scheme to improve figures of merit such as power consumption, area and delay. To further save hardware resources, a so-called eliminated approximate hybrid divider (E-AXHD) based on AXHD is also proposed. In this improved design, a reduced width divider is used to replace the EXD in AXHD. Specifically, for a 16-by-8 design, n=(n + 1) array division is used to replace the n=8 array division (n < 8). The proposed AXHD and E-AXHD are evaluated and analyzed using error and hardware metrics. The proposed designs are also compared with EXD, LD and previous approximate dividers. The results show that the proposed designs outperform previous approximate dividers by considering both energy and error. The proposed hybrid dividers are of particular interest for error tolerant applications such as image processing and machine learning

Improving the Hardware Performance of Arithmetic Circuits using Approximate Computing

An application that can produce a useful result despite some level of computational error is said to be error resilient. Approximate computing can be applied to error resilient applications by intentionally introducing error to the computation in order to improve performance, and it has been shown that approximation is especially well-suited for application in arithmetic computing hardware. In this thesis, novel approximate arithmetic architectures are proposed for three different operations, namely multiplication, division, and the multiply accumulate (MAC) operation. For all designs, accuracy is evaluated in terms of mean relative error distance (MRED) and normalized mean error distance (NMED), while hardware performance is reported in terms of critical path delay, area, and power consumption. Three approximate Booth multipliers (ABM-M1, ABM-M2, ABM-M3) are designed in which two novel inexact partial product generators are used to reduce the dimensions of the partial product matrix. The proposed multipliers are compared to other state-of-the-art designs in terms of both accuracy and hardware performance, and are found to reduce power consumption by up to 56% when compared to the exact multiplier. The function of the multipliers is verified in several image processing applications. Two approximate restoring dividers (AXRD-M1, AXRD-M2) are proposed along with a novel inexact restoring divider cell. In the first divider, the conventional cells are replaced with the proposed inexact cells in several columns. The second divider computes only a subset of the trial subtractions, after which the divisor and partial remainder are rounded and encoded so that they may be used to estimate the remaining quotient bits. The proposed dividers are evaluated for accuracy and hardware performance alongside several benchmarking designs, and their function is verified using change detection and foreground extraction applications. An approximate MAC unit is presented in which the multiplication is implemented using a modified version of ABM-M3. The delay is reduced by using a fused architecture where the accumulator is summed as part of the multiplier compression. The accuracy and hardware savings of the MAC unit are measured against several works from the literature, and the design is utilized in a number of convolution operations

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey

Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications

The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. In Part II of our survey, we classify and present the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators & systems. Moreover, we present a detailed analysis of the application spectrum of Approximate Computing and discuss open challenges and future directions.Comment: Under Review at ACM Computing Survey

IEEE Compliant Double-Precision FPU and 64-bit ALU with Variable Latency Integer Divider

Together the arithmetic logic unit (ALU) and floating-point unit (FPU) perform all of the mathematical and logic operations of computer processors. Because they are used so prominently, they fall in the critical path of the central processing unit - often becoming the bottleneck, or limiting factor for performance. As such, the design of a high-speed ALU and FPU is vital to creating a processor capable of performing up to the demanding standards of today\u27s computer users. In this paper, both a 64-bit ALU and a 64-bit FPU are designed based on the reduced instruction set computer architecture. The ALU performs the four basic mathematical operations - addition, subtraction, multiplication and division - in both unsigned and two\u27s complement format, basic logic operations and shifting. The division algorithm is a novel approach, using a comparison multiples based SRT divider to create a variable latency integer divider. The floating-point unit performs the double-precision floating-point operations add, subtract, multiply and divide, in accordance with the IEEE 754 standard for number representation and rounding. The ALU and FPU were implemented in VHDL, simulated in ModelSim, and constrained and synthesized using Synopsys Design Compiler (2006.06). They were synthesized using TSMC 0.1 3nm CMOS technology. The timing, power and area synthesis results were recorded, and, where applicable, compared to those of the corresponding DesignWare components.The ALU synthesis reported an area of 122,215 gates, a power of 384 mW, and a delay of 2.89 ns - a frequency of 346 MHz. The FPU synthesis reported an area 84,440 gates, a delay of 2.82 ns and an operating frequency of 355 MHz. It has a maximum dynamic power of 153.9 mW

Design and optimization of approximate multipliers and dividers for integer and floating-point arithmetic

The dawn of the twenty-first century has witnessed an explosion in the number of digital devices and data. While the emerging deep learning algorithms to extract information from this vast sea of data are becoming increasingly compute-intensive, traditional means of improving computing power are no longer yielding gains at the same rate due to the diminishing returns from traditional technology scaling. To minimize the increasing gap between computational demands and the available resources, the paradigm of approximate computing is emerging as one of the potential solutions. Specifically, the resource-efficient approximate arithmetic units promise overall system efficiency, since most of the compute-intensive applications are dominated by arithmetic operations. This thesis primarily presents design techniques for approximate hardware multipliers and dividers. The thesis presents the design of two approximate integer multipliers and an approximate integer divider. These are: an error-configurable minimally-biased approximate integer multiplier (MBM), an error-configurable reduced-error approximate log based multiplier (REALM), and error-configurable integer divider INZeD. The two multiplier designs and the divider designs are based on the coupling of novel mathematically formulated error-reduction mechanisms in the classical approximate log based multiplier and dividers, respectively. They exhibit very low error bias and offer Pareto-optimal error vs. resource-efficiency trade-offs when compared with the state-of-the-art approximate integer multipliers/dividers. Further, the thesis also presents design of approximate floating-point multipliers and dividers. These designs utilize the optimized versions of the proposed MBM and REALM multipliers for mantissa multiplications and the proposed INZeD divider for mantissa division, and offer better design trade-offs than traditional precision scaling. The existing approximate integer dividers as well as the proposed INZeD suffer from unreasonably high worst-case error. This thesis presents WEID, which is a novel light-weight method for reducing worst-case error in approximate dividers. Finally, the thesis presents a methodology for selection of approximate arithmetic units for a given application. The methodology is based on a novel selection algorithm and utilizes the subrange error characterization of approximate arithmetic units, which performs error characterization independently in different segments of the input range

Integrated optical motion detection

Two systems for detecting the motion of a scene are described. For both, an image is projected directly onto an integrated circuit that contains photosensors and computing circuitry to extract the motion. The first system, which has been reported earlier, correlates the analog image with a digitized version of the image stored from the previous cycle. The chip reports the motion that corresponds to the maximum analog correlation value. This system represents an advance from previous designs but exhibits some shortcomings. A second completely analog design surpasses the first. The mathematical foundation is derived and the CMOS circuits used in the implementation are given. Test results and characterization of the working chips are reported. The new motion detector is not clocked and exhibits collective behavior. The extensive use of local information avoids the correspondence problem. The system can be thought of as a Hopfield neural net with one important extension--input-driven synapses. The motion detector also meshes nicely with the existing computational vision work. Extensions to handle more complex motions are proposed. The suitability of the motion-extraction algorithm as a biological vision model is explored

Comparison of logarithmic and floating-point number systems implemented on Xilinx Virtex-II field-programmable gate arrays

The aim of this thesis is to compare the implementation of parameterisable LNS (logarithmic number system) and floating-point high dynamic range number systems on FPGA. The Virtex/Virtex-II range of FPGAs from Xilinx, which are the most popular FPGA technology, are used to implement the designs. The study focuses on using the low level primitives of the technology in an efficient way and so initially the design issues in implementing fixed-point operators are considered. The four basic operations of addition, multiplication, division and square root are considered. Carry- free adders, ripple-carry adders, parallel multipliers and digit recurrence division and square root are discussed. The floating-point operators use the word format and exceptions as described by the IEEE std-754. A dual-path adder implementation is described in detail, as are floating-point multiplier, divider and square root components. Results and comparisons with other works are given. The efficient implementation of function evaluation methods is considered next. An overview of current FPGA methods is given and a new piecewise polynomial implementation using the Taylor series is presented and compared with other designs in the literature. In the next section the LNS word format, accuracy and exceptions are described and two new LNS addition/subtraction function approximations are described. The algorithms for performing multiplication, division and powering in the LNS domain are also described and are compared with other designs in the open literature. Parameterisable conversion algorithms to convert to/from the fixed-point domain from/to the LNS and floating-point domain are described and implementation results given. In the next chapter MATLAB bit-true software models are given that have the exact functionality as the hardware models. The interfaces of the models are given and a serial communication system to perform low speed system tests is described. A comparison of the LNS and floating-point number systems in terms of area and delay is given. Different functions implemented in LNS and floating-point arithmetic are also compared and conclusions are drawn. The results show that when the LNS is implemented with a 6-bit or less characteristic it is superior to floating-point. However, for larger characteristic lengths the floating-point system is more efficient due to the delay and exponential area increase of the LNS addition operator. The LNS is beneficial for larger characteristics than 6-bits only for specialist applications that require a high portion of division, multiplication, square root, powering operations and few additions

Wideband CMOS Data Converters for Linear and Efficient mmWave Transmitters

With continuously increasing demands for wireless connectivity, higher\ua0carrier frequencies and wider bandwidths are explored. To overcome a limited transmit power at these higher carrier frequencies, multiple\ua0input multiple output (MIMO) systems, with a large number of transmitters\ua0and antennas, are used to direct the transmitted power towards\ua0the user. With a large transmitter count, each individual transmitter\ua0needs to be small and allow for tight integration with digital circuits. In\ua0addition, modern communication standards require linear transmitters,\ua0making linearity an important factor in the transmitter design.In this thesis, radio frequency digital-to-analog converter (RF-DAC)-based transmitters are explored. They shift the transition from digital\ua0to analog closer to the antennas, performing both digital-to-analog\ua0conversion and up-conversion in a single block. To reduce the need for\ua0computationally costly digital predistortion (DPD), a linear and wellbehaved\ua0RF-DAC transfer characteristic is desirable. The combination\ua0of non-overlapping local oscillator (LO) signals and an expanding segmented\ua0non-linear RF-DAC scaling is evaluated as a way to linearize\ua0the transmitter. This linearization concept has been studied both for\ua0the linearization of the RF-DAC itself and for the joint linearization of\ua0the cascaded RF-DAC-based modulator and power amplifier (PA) combination.\ua0To adapt the linearization, observation receivers are needed.\ua0In these, high-speed analog-to-digital converters (ADCs) have a central\ua0role. A high-speed ADC has been designed and evaluated to understand\ua0how concepts used to increase the sample rate affect the dynamic performance

Energy-efficient embedded machine learning algorithms for smart sensing systems

Embedded autonomous electronic systems are required in numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical systems. Embedded electronic systems usually host sensors, and each sensor hosts multiple input channels (e.g., tactile, vision), tightly coupled to the electronic computing unit (ECU). The ECU extracts information by often employing sophisticated methods, e.g., Machine Learning. However, embedding Machine Learning algorithms poses essential challenges in terms of hardware resources and energy consumption because of: 1) the high amount of data to be processed; 2) computationally demanding methods. Leveraging on the trade-off between quality requirements versus computational complexity and time latency could reduce the system complexity without affecting the performance. The objectives of the thesis are to develop: 1) energy-efficient arithmetic circuits outperforming state of the art solutions for embedded machine learning algorithms, 2) an energy-efficient embedded electronic system for the \u201celectronic-skin\u201d (e-skin) application. As such, this thesis exploits two main approaches: Approximate Computing: In recent years, the approximate computing paradigm became a significant major field of research since it is able to enhance the energy efficiency and performance of digital systems. \u201cApproximate Computing\u201d(AC) turned out to be a practical approach to trade accuracy for better power, latency, and size . AC targets error-resilient applications and offers promising benefits by conserving some resources. Usually, approximate results are acceptable for many applications, e.g., tactile data processing,image processing , and data mining ; thus, it is highly recommended to take advantage of energy reduction with minimal variation in performance . In our work, we developed two approximate multipliers: 1) the first one is called \u201cMETA\u201d multiplier and is based on the Error Tolerant Adder (ETA), 2) the second one is called \u201cApproximate Baugh-Wooley(BW)\u201d multiplier where the approximations are implemented in the generation of the partial products. We showed that the proposed approximate arithmetic circuits could achieve a relevant reduction in power consumption and time delay around 80.4% and 24%, respectively, with respect to the exact BW multiplier. Next, to prove the feasibility of AC in real world applications, we explored the approximate multipliers on a case study as the e-skin application. The e-skin application is defined as multiple sensing components, including 1) structural materials, 2) signal processing, 3) data acquisition, and 4) data processing. Particularly, processing the originated data from the e-skin into low or high-level information is the main problem to be addressed by the embedded electronic system. Many studies have shown that Machine Learning is a promising approach in processing tactile data when classifying input touch modalities. In our work, we proposed a methodology for evaluating the behavior of the system when introducing approximate arithmetic circuits in the main stages (i.e., signal and data processing stages) of the system. Based on the proposed methodology, we first implemented the approximate multipliers on the low-pass Finite Impulse Response (FIR) filter in the signal processing stage of the application. We noticed that the FIR filter based on (Approx-BW) outperforms state of the art solutions, while respecting the tradeoff between accuracy and power consumption, with an SNR degradation of 1.39dB. Second, we implemented approximate adders and multipliers respectively into the Coordinate Rotational Digital Computer (CORDIC) and the Singular Value Decomposition (SVD) circuits; since CORDIC and SVD take a significant part of the computationally expensive Machine Learning algorithms employed in tactile data processing. We showed benefits of up to 21% and 19% in power reduction at the cost of less than 5% accuracy loss for CORDIC and SVD circuits when scaling the number of approximated bits. 2) Parallel Computing Platforms (PCP): Exploiting parallel architectures for near-threshold computing based on multi-core clusters is a promising approach to improve the performance of smart sensing systems. In our work, we exploited a novel computing platform embedding a Parallel Ultra Low Power processor (PULP), called \u201cMr. Wolf,\u201d for the implementation of Machine Learning (ML) algorithms for touch modalities classification. First, we tested the ML algorithms at the software level; for RGB images as a case study and tactile dataset, we achieved accuracy respectively equal to 97% and 83.5%. After validating the effectiveness of the ML algorithm at the software level, we performed the on-board classification of two touch modalities, demonstrating the promising use of Mr. Wolf for smart sensing systems. Moreover, we proposed a memory management strategy for storing the needed amount of trained tensors (i.e., 50 trained tensors for each class) in the on-chip memory. We evaluated the execution cycles for Mr. Wolf using a single core, 2 cores, and 3 cores, taking advantage of the benefits of the parallelization. We presented a comparison with the popular low power ARM Cortex-M4F microcontroller employed, usually for battery-operated devices. We showed that the ML algorithm on the proposed platform runs 3.7 times faster than ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15 7 better energy efficiency than the classification done on the STM32F40, consuming 81mJ per classification and 150 pJ per operation