Ultra Low Energy Analog Image Processing Using Spin Neurons
Mrigank Sharad, Charles Augustine, Georgios Panagopoulos, Kaushik Roy

TL;DR
This paper introduces an ultra low energy analog image processing architecture using spin neurons, achieving significant energy reduction compared to traditional CMOS methods, and is the first to apply nano magnets in this context.
Contribution
Proposes a novel spin neuron-based analog image processing system that drastically reduces energy consumption and demonstrates its effectiveness through physics-based simulations.
Findings
Over two orders of magnitude energy reduction compared to CMOS
Successful simulation of feature extraction and image processing tasks
First application of nano magnets in analog signal processing
Abstract
In this work we present an ultra low energy, 'on-sensor' image processing architecture, based on cellular array of spin based neurons. The 'neuron' constitutes of a lateral spin valve (LSV) with multiple input magnets, connected to an output magnet, using metal channels. The low resistance, magneto-metallic neurons operate at a small terminal voltage of ~20mV, while performing analog computation upon photo sensor inputs. The static current-flow across the device terminals is limited to small periods, corresponding to magnet switching time, and, is determined by a low duty-cycle system-clock. Thus, the energy-cost of analog-mode processing, inevitable in most image sensing applications, is reduced and made comparable to that of dynamic and leakage power consumption in peripheral CMOS units. Performance of the proposed architecture for some common image sensing and processing applications…
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