Design of Low-Voltage Digital Building Blocks and ADCs for Energy-Efficient Systems

Increasing number of energy-limited applications continue to drive the demand for designing systems with high energy efficiency. This tutorial covers the main building blocks of a system implementation including digital logic, embedded memories, and analog-to-digital converters and describes the challenges and solutions to designing these blocks for low-voltage operation

Overparameterization: A Connection Between Software 1.0 and Software 2.0

A new ecosystem of machine-learning driven applications, titled Software 2.0, has arisen that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional machine learning tasks. However, these techniques have also grown to include other structured domains, such as program analysis and program optimization for which novel, domain-specific insights mate with model design. In this paper, we connect the world of Software 2.0 with that of traditional software - Software 1.0 - through overparameterization: a program may provide more computational capacity and precision than is necessary for the task at hand. In Software 2.0, overparamterization - when a machine learning model has more parameters than datapoints in the dataset - arises as a contemporary understanding of the ability for modern, gradient-based learning methods to learn models over complex datasets with high-accuracy. Specifically, the more parameters a model has, the better it learns. In Software 1.0, the results of the approximate computing community show that traditional software is also overparameterized in that software often simply computes results that are more precise than is required by the user. Approximate computing exploits this overparameterization to improve performance by eliminating unnecessary, excess computation. For example, one - of many techniques - is to reduce the precision of arithmetic in the application. In this paper, we argue that the gap between available precision and that that is required for either Software 1.0 or Software 2.0 is a fundamental aspect of software design that illustrates the balance between software designed for general-purposes and domain-adapted solutions. A general-purpose solution is easier to develop and maintain versus a domain-adapted solution. However, that ease comes at the expense of performance. We show that the approximate computing community and the machine learning community have developed overlapping techniques to improve performance by reducing overparameterization. We also show that because of these shared techniques, questions, concerns, and answers on how to construct software can translate from one software variant to the other

Real-Time Scheduling Algorithm Design on Stochastic Processors

Recent studies have shown that significant power savings are possible with the use of in- exact processors, which may contain a small percentage of errors in computation. However, use of such processors in time-sensitive systems is challenging as these processors significantly hamper the system performance. In this thesis, a design framework is developed for real-time applications running on stochastic processors. To identify hardware error pat- terns, two methods are proposed to predict the occurrence of hardware errors. In addition, an algorithm is designed that uses knowledge of the hardware error patterns to judiciously schedule real-time jobs in order to maximize real-time performance. Both analytical and simulation results show that the proposed approach provides significant performance improvements when compared to an existing real-time scheduling algorithm and is efficient enough for online use

Reliability-Aware Optimization of Approximate Computational Kernels with Rely

Emerging high-performance architectures are anticipated to contain unreliable components (e.g., ALUs) that offer low power consumption at the expense of soft errors. Some applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors and can therefore trade accuracy of their results for reduced energy consumption by utilizing these unreliable hardware components. We present and evaluate a technique for reliability-aware optimization of approximate computational kernel implementations. Our technique takes a standard implementation of a computation and automatically replaces some of its arithmetic operations with unreliable versions that consume less power, but may produce incorrect results with some probability. Our technique works with a developer-provided specification of the required reliability of a computation -- the probability that it returns the correct result -- and produces an unreliable implementation that satisfies that specification. We evaluate our approach on five applications from the image processing, numerical analysis, and financial analysis domains and demonstrate how our technique enables automatic exploration of the trade-off between the reliability of a computation and its performance

Precision-Energy-Throughput Scaling Of Generic Matrix Multiplication and Convolution Kernels Via Linear Projections

Generic matrix multiplication (GEMM) and one-dimensional convolution/cross-correlation (CONV) kernels often constitute the bulk of the compute- and memory-intensive processing within image/audio recognition and matching systems. We propose a novel method to scale the energy and processing throughput of GEMM and CONV kernels for such error-tolerant multimedia applications by adjusting the precision of computation. Our technique employs linear projections to the input matrix or signal data during the top-level GEMM and CONV blocking and reordering. The GEMM and CONV kernel processing then uses the projected inputs and the results are accumulated to form the final outputs. Throughput and energy scaling takes place by changing the number of projections computed by each kernel, which in turn produces approximate results, i.e. changes the precision of the performed computation. Results derived from a voltage- and frequency-scaled ARM Cortex A15 processor running face recognition and music matching algorithms demonstrate that the proposed approach allows for 280%~440% increase of processing throughput and 75%~80% decrease of energy consumption against optimized GEMM and CONV kernels without any impact in the obtained recognition or matching accuracy. Even higher gains can be obtained if one is willing to tolerate some reduction in the accuracy of the recognition and matching applications

Verifying Quantitative Reliability of Programs That Execute on Unreliable Hardware

Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and recovery from soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. In this paper we present Rely, a programming language that enables developers to reason about the quantitative reliability of an application -- namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification, that characterizes the reliability of the underlying hardware components, and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely.This research was supported in part by the National Science Foundation (Grants CCF-0905244, CCF-1036241, CCF-1138967, CCF-1138967, and IIS-0835652), the United States Department of Energy (Grant DE-SC0008923), and DARPA (Grants FA8650-11-C-7192, FA8750-12-2-0110)

Chisel: Reliability- and Accuracy-Aware Optimization of Approximate Computational Kernels

The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy. We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification. We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees.National Science Foundation (U.S.) (Grant CCF-1036241)National Science Foundation (U.S.) (Grant CCF-1138967)National Science Foundation (U.S.) (Grant IIS-0835652)United States. Dept. of Energy (Grant DE-SC0008923)United States. Defense Advanced Research Projects Agency (Grant FA8650-11-C-7192)United States. Defense Advanced Research Projects Agency (Grant FA8750-12-2-0110)United States. Defense Advanced Research Projects Agency (Grant FA-8750-14-2-0004