60 research outputs found
Value-Deviation-Bounded Serial Data Encoding for Energy-Efficient Approximate Communication
Transferring data between ICs accounts for a growing proportion of system power in wearable and mobile systems. Reducing signal transitions reduces the dynamic power dissipated in this data transfer, but traditional approaches cannot be applied when the transfer interfaces are serial buses. To address this challenge, we present a family of optimal value-deviation-bounded approximate serial encoders (VDBS encoders) that significantly reduce signal transitions (and hence, dynamic power) for bit-serial communication interfaces. When the data in transfer are from sensors, VDBS encoding enables a tradeoff between power efficiency and application fidelity, by exploiting the tolerance of many of the typical algorithms consuming sensor data to deviations in values. We derive analytic formulations for the family of VDBS encoders and introduce an efficient algorithm that performs close to the Pareto-optimal encoders. We evaluate the algorithm in two applications: Encoding data between a camera and processor in a text-recognition system, and between an accelerometer and processor in a pedometer system. For the text recognizer, the algorithm reduces signal transitions by 55% on average, while maintaining OCR accuracy at over 90% for previously-correctly-recognized text. For the pedometer, the algorithm reduces signal transitions by an average of 54% in exchange for step count errors of under 5%
Warp: A Hardware Platform for Efficient Multi- Modal Sensing with Adaptive Approximation
We present Warp, the first open hardware platform designed explicitly to support research in approximate computing. Warp incorporates 21 sensors, computation, and circuit-level facilities designed explicitly to enable approximate computing research, in a 3.6 cm×3.3 cm×0.5
cm area. Warp uses these facilities to support a wide range of precision and accuracy versus power and performance tradeoffs
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Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication
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
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Warp: A Hardware Platform for Efficient Multi- Modal Sensing with Adaptive Approximation
We present Warp, the first open hardware platform designed explicitly to support research in approximate computing. Warp incorporates 21 sensors, computation, and circuit-level facilities designed explicitly to enable approximate computing research, in a 3.6 cm×3.3 cm×0.5
cm area. Warp uses these facilities to support a wide range of precision and accuracy versus power and performance tradeoffs
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