autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary approximate circuits have already been proposed to simplify the design process of approximate accelerators. Because these libraries contain from tens to thousands of approximate implementations for a single arithmetic operation it is intractable to find an optimal combination of approximate circuits in the library even for an application consisting of a few operations. An open problem is "how to effectively combine circuits from these libraries to construct complex approximate accelerators". This paper proposes a novel methodology for searching, selecting and combining the most suitable approximate circuits from a set of available libraries to generate an approximate accelerator for a given application. To enable fast design space generation and exploration, the methodology utilizes machine learning techniques to create computational models estimating the overall quality of processing and hardware cost without performing full synthesis at the accelerator level. Using the methodology, we construct hundreds of approximate accelerators (for a Sobel edge detector) showing different but relevant tradeoffs between the quality of processing and hardware cost and identify a corresponding Pareto-frontier. Furthermore, when searching for approximate implementations of a generic Gaussian filter consisting of 17 arithmetic operations, the proposed approach allows us to identify approximately $10^3$ highly important implementations from $10^{23}$ possible solutions in a few hours, while the exhaustive search would take four months on a high-end processor.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US

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

Automatic synthesis of analog layout : a survey

A review of recent research in the automatic synthesis of physical geometry for analog integrated circuits is presented. On introduction, an explanation of the difficulties involved in analog layout as opposed to digital layout is covered. Review of the literature then follows. Emphasis is placed on the exposition of general methods for addressing problems specific to analog layout, with the details of specific systems only being given when they surve to illustrate these methods well. The conclusion discusses problems remaining and offers a prediction as to how technology will evolve to solve them. It is argued that although progress has been and will continue to be made in the automation of analog IC layout, due to fundamental differences in the nature of analog IC design as opposed to digital design, it should not be expected that the level of automation of the former will reach that of the latter any time soon

SIM-DSP: A DSP-Enhanced CAD Platform for Signal Integrity Macromodeling and Simulation

Macromodeling-Simulation process for signal integrity verifications has become necessary for the high speed circuit system design. This paper aims to introduce a “VLSI Signal Integrity Macromodeling and Simulation via Digital Signal Processing Techniques” framework (known as SIM-DSP framework), which applies digital signal processing techniques to facilitate the SI verification process in the pre-layout design phase. Core identification modules and peripheral (pre-/post-)processing modules have been developed and assembled to form a verification flow. In particular, a single-step discrete cosine transform truncation (DCTT) module has been developed for modeling-simulation process. In DCTT, the response modeling problem is classified as a signal compression problem, wherein the system response can be represented by a truncated set of non-pole based DCT bases, and error can be analyzed through Parseval’s theorem. Practical examples are given to show the applicability of our proposed framework

Yield Model Characterization For Analog Integrated Circuit Using Pareto-Optimal Surface

A novel technique is proposed in this paper that achieves a yield optimized design from a set of optimal performance points on the Pareto front. Trade-offs among performance functions are explored through multi-objective optimization and Monte Carlo simulation is used to find the design point producing the best overall yield. One advantage of the approach presented is a reduction in the computational cost normally associated with Monte Carlo simulation. The technique offers a yield optimized robust circuit design solution with transistor level accuracy. An example using an OTA is presented to demonstrate the effectiveness of the work

In defense of compilation: A response to Davis' form and content in model-based reasoning

In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning

Issues in the Scalability of Gate-level Morphogenetic Evolvable Hardware

Traditional approaches to evolvable hardware (EHW), in which the field programmable gate array (FPGA) configuration is directly encoded, have not scaled well with increasing circuit and FPGA complexity. To overcome this there have been moves towards encoding a growth process, known as morphogenesis. Using a morphogenetic approach, has shown success in scaling gate-level EHW for a signal routing problem, however, when faced with a evolving a one-bit full adder, unforseen difficulties were encountered. In this paper, we provide a measurement of EHW problem difficulty that takes into account the salient features of the problem, and when combined with a measure of feedback from the fitness function, we are able to estimate whether or not a given EHW problem is likely to be able to be solved successfully by our morphogenetic approach. Using these measurements we are also able to give an indication of the scalability of morphogenesis when applied to EHW