A Survey Of Techniques for Approximate Computing and Storage

sparsh

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A Survey Of Techniques for Approximate Computing accepted in ACM Computing Surveys 2016, reviews ~85 papers.

Covers:
* Approximate computing in CPU, GPU and FPGA and various processor components (e.g. cache, main memory, secondary storage)
* Approximate storage in SRAM, DRAM/eDRAM, non-volatile memories, e.g. Flash, STT-RAM, ReRAM etc.
* Approximate circuits, e.g. adders, multipliers, etc. and neural-networks based approximation
and more.

Part of the abstract:
Approximate computing trades off computation quality with the effort expended and as rising performance demands confront with plateauing resource budgets, approximate computing has become, not merely attractive, but even imperative. In this paper, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units, processor components, memory technologies etc., and programming frameworks for AC.

The aim of this paper is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.
 
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