AxMemo: Hardware-compiler co-design for approximate code memoization

Zhenhong Liu, Amir Yazdanbakhsh, Dong Kai Wang, Hadi Esmaeilzadeh, Nam Sung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Historically, continuous improvements in general-purpose processors have fueled the economic success and growth of the IT industry. However, the diminishing benefits from transistor scaling and conventional optimization techniques necessitates moving beyond common practices. Approximate computing is one such unconventional technique that has shown promise in pushing the boundaries of general-purpose processing. This paper sets out to employ approximation for processors that are commonly used in cyber-physical domains and may become building blocks of Internet of Things. To this end, we propose AxMemo to exploit the computation redundancy that stems from data similarity in the inputs of code blocks. Such input behavior is prevalent in cyber-physical systems as they deal with real-world data that naturally harbors redundancy. Therefore, in contrast to existing memoization techniques that replace costly floating-point arithmetic operations with limited number of inputs, AxMemo focuses on memoizing blocks of code with potentially many inputs. As such, AxMemo aims to replace long sequences of instructions with a few hash and lookup operations. By reducing the number of dynamic instructions, AxMemo alleviates the von Neumann and execution overheads of passing instructions through the processor pipeline altogether. The challenge AxMemo facing is to provide low-cost hashing mechanisms that can generate rather unique signature for each multi-input combination. To address this challenge, we develop a novel use of Cyclic Redundancy Checking (CRC) to hash the inputs. To increase lookup table hit rate, AxMemo employs a two-level memoization lookup, which utilizes small dedicated SRAM and spare storage in the last level cache. These solutions enable AxMemo to efficiently memoize relatively large code regions with variable input sizes and types using the same underlying hardware. Our experiment shows that AxMemo offers 2.64× speedup and 2.58 × energy reduction with mere 0.2% of quality loss averaged across ten benchmarks. These benefits come with an area overhead of just 2.1%.

Original languageEnglish (US)
Title of host publicationISCA 2019 - Proceedings of the 2019 46th International Symposium on Computer Architecture
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages685-697
Number of pages13
ISBN (Electronic)9781450366694
DOIs
StatePublished - Jun 22 2019
Event46th International Symposium on Computer Architecture, ISCA 2019 - Phoenix, United States
Duration: Jun 22 2019Jun 26 2019

Publication series

NameProceedings - International Symposium on Computer Architecture
ISSN (Print)1063-6897

Conference

Conference46th International Symposium on Computer Architecture, ISCA 2019
CountryUnited States
CityPhoenix
Period6/22/196/26/19

Keywords

  • Approximate computing
  • Hardware-software co-design
  • Memoization

ASJC Scopus subject areas

  • Hardware and Architecture

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