Paper Info
Title
Unsupervised Digit Recognition Using Cosine Similarity In A Neuromemristive Competitive Learning System
Abstract
AbstractThis work addresses how to naturally adopt the l2-norm cosine similarity in the neuromemristive system and studies the unsupervised learning performance on handwritten digit image recognition. Proposed architecture is a two-layer fully connected neural network with a hard winner-take-all (WTA) learning module. For input layer, we propose single-spike temporal code that transforms input stimuli into the set of single spikes with different latencies and voltage levels. For a synapse model, we employ a compound memristor where stochastically switching binary-state memristors connected in parallel, which offers a reliable and scalable multi-state solution for synaptic weight storage. Hardware-friendly synaptic adaptation mechanism is proposed to realize spike-timing-dependent plasticity learning. Input spikes are sent out through those memristive synapses to each and every integrate-and-fire neuron in the fully connected output layer, where the hard WTA network motif introduces the competition based on cosine similarity for the given input stimuli. Finally, we present 92.64% accuracy performance on unsupervised digit recognition with only single-epoch MNIST dataset training via high-level simulations, including extensive analysis on the impact of system parameters.
Year
DOI
Venue
2022
10.1145/3473036
ACM Journal on Emerging Technologies in Computing Systems
Keywords
DocType
Volume
Unsupervised learning, MNIST, digit recognition, single-spike temporal code, spiking neural network, compound memristor, winner-take-all, neuromorphic computing
Journal
18
Issue
ISSN
Citations 
2
1550-4832
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
BW Ku100.34
CD Schuman200.34
MM Adnan300.34
TM Mintz400.34
R Pooser500.34
KE Hamilton600.34