4 research outputs found
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
Leveraging Hardware Caches for Memoization
Memoization improves performance and saves energy by caching and reusing the outputs of repetitive computations. Prior work has proposed software and hardware memoization techniques, but both have significant drawbacks. Software memoization suffers from high runtime overheads, and is thus limited to long computations. Conventional hardware memoization techniques achieve low overheads and can memoize short functions, but they rely on large, special-purpose memoization caches that waste significant area and energy. We propose MCACHE, a hardware technique that leverages data caches for memoization. MCACHE stores memoization tables in memory, and allows them to share cache capacity with normal program data. MCACHE introduces ISA and pipeline extensions to accelerate memoization operations, bridging the gap between software and conventional hardware techniques. Simulation results show that MCACHE improves performance by up to 21 ×, outperforms software memoization by up to 2.2 ×, and achieves similar or superior performance over conventional hardware techniques without any dedicated storage
Leveraging Hardware Caches for Memoization
Memoization improves performance and saves energy by caching and reusing the outputs of repetitive computations. Prior work has proposed software and hardware memoization techniques, but both have significant drawbacks. Software memoization suffers from high runtime overheads, and is thus limited to long computations. Conventional hardware memoization techniques achieve low overheads and can memoize short functions, but they rely on large, special-purpose memoization caches that waste significant area and energy. We propose MCACHE, a hardware technique that leverages data caches for memoization. MCACHE stores memoization tables in memory, and allows them to share cache capacity with normal program data. MCACHE introduces ISA and pipeline extensions to accelerate memoization operations, bridging the gap between software and conventional hardware techniques. Simulation results show that MCACHE improves performance by up to 21 ×, outperforms software memoization by up to 2.2 ×, and achieves similar or superior performance over conventional hardware techniques without any dedicated storage