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Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Stanley-Marbell, P 

Abstract

Computing systems that can tolerate effects of errors in their communicated data values can trade this tolerance for improved resource efficiency. Many important applications of computing, such as embedded sensor systems, can tolerate errors that are bounded in their distribution of deviation from correctness (distortion). We present a channel adaptation technique which modulates properties of I/O channels typical in embedded sensor systems, to provide a tradeoff between I/O power dissipation and distortion of communicated data. We provide an efficient-to-compute formulation for the distribution of integer distortion accounting for the distribution of transmitted values. Using this formulation we implement our value-deviation-bounded (VDB) channel adaptation. We experimentally quantify the achieved reduction in power dissipation on a hardware prototype integrated with the required programmable channel modulation circuitry. We augment these experimental measurements with an analysis of the distributions of distortions. We show that our probabilistic VDB channel adaptation can provide up to a 2× reduction in I/O power dissipation. When synthesized for a miniature low-power FPGA intended for use in sensor interfaces, a register transfer level implementation of the channel adaptation control logic requires only 106 flip-flops and 224 4-input LUTs for implementing per-bit channel adaptation on serialized streams of 8-bit sensor data.

Description

Keywords

46 Information and Computing Sciences, 40 Engineering, 4008 Electrical Engineering, 4009 Electronics, Sensors and Digital Hardware

Journal Title

IEEE Transactions on Computers

Conference Name

Journal ISSN

0018-9340
1557-9956

Volume Title

70

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Rights

All rights reserved
Sponsorship
Alan Turing Institute (EP/N510129/1)
Royal Society (RG170136)