Hybrid Temporal Computing for Lower Power Hardware Accelerators
Maliha Tasnim, Sachin Sachdeva, Yibo Liu, Sheldon X.-D. Tan

TL;DR
This paper introduces a hybrid temporal computing framework that combines pulse rate and temporal data encoding to create ultra-low energy hardware accelerators capable of efficient arithmetic operations and image processing tasks.
Contribution
The paper presents a novel HTC framework that overcomes limitations of race logic, enabling a wide range of arithmetic operations with reduced power and area in hardware accelerators.
Findings
HTC MAC reduces power by 45.2% and area by 50.13% compared to CBSC MAC.
HTC FIR filter outperforms unary design on all metrics, reducing power by 36.61%.
HTC DCT filter maintains image quality while reducing power by 23.34%.
Abstract
In this paper, we propose a new hybrid temporal computing (HTC) framework that leverages both pulse rate and temporal data encoding to design ultra-low energy hardware accelerators. Our approach is inspired by the recently proposed temporal computing, or race logic, which encodes data values as single delays, leading to significantly lower energy consumption due to minimized signal switching. However, race logic is limited in its applications due to inherent restrictions. The new HTC framework overcomes these limitations by encoding signals in both temporal and pulse rate formats for multiplication and in temporal format for propagation. This approach maintains reduced switch energy while being general enough to implement a wide range of arithmetic operations. We demonstrate how HTC multiplication is performed for both unipolar and bipolar data encoding and present the basic designs for…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Embedded Systems Design Techniques · Advanced Memory and Neural Computing
