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

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.

Research and Application of General Information Measures Based on a Unified Model

“…The distortion of information is an important measure of error and plays an important role in the development of information systems [43]. The distortion of restorable information 𝐼 = 〈𝑜, 𝑇 ℎ , 𝑓, 𝑐, 𝑇 𝑚 , 𝑔〉 can be defined as the distance between its reflected state and its restored state in 10 > distance space; i.e., distortion 𝐽 (𝐼) = d(𝑓, 𝑓 ̃), where 𝑓 ̃(𝑜 ̃, 𝑇 ℎ ̃)…”