Linear Delay-cell Design for Low-energy Delay Multiplication and Accumulation
Aditya Shukla

TL;DR
This paper introduces a novel linear delay-cell design for low-energy delay multiplication and accumulation, enabling energy-efficient analog MAC operations suitable for deep neural network inference on constrained devices.
Contribution
A new linearly tunable delay-cell using an inverted MOS capacitor is proposed, with analytical modeling, jitter analysis, and a biasing circuit to enable scalable delay-multiplier implementation.
Findings
The delay-cell achieves near state-of-the-art energy consumption.
The design supports scalable bit-wise delay adjustments.
Simulations validate the feasibility of the proposed approach.
Abstract
A practical deep neural network's (DNN) evaluation involves thousands of multiply-and-accumulate (MAC) operations. To extend DNN's superior inference capabilities to energy constrained devices, architectures and circuits that minimize energy-per-MAC must be developed. In this respect, analog delay-based MAC is advantageous due to reasons both extrinsic and intrinsic to the MAC implementation - (1) lower fixed-point precision requirement for a DNN's evaluation, (2) better dynamic range than charge-based accumulation, for smaller technology nodes, and (3) simpler analog-digital interfacing. Implementing DNNs using delay-based MAC requires mixed-signal delay multipliers that accept digitally stored weights and analog voltages as arguments. To this end, a novel, linearly tune-able delay-cell is proposed, wherein, the delay is realized using an inverted MOS capacitor's (C*) steady discharge…
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Taxonomy
TopicsAnalog and Mixed-Signal Circuit Design · Advanced Memory and Neural Computing · Low-power high-performance VLSI design
