An Adaptive Synaptic Array using Fowler-Nordheim Dynamic Analog Memory
In this paper we present a synaptic array that uses dynamical states to implement an analog memory for energy-efficient training of machine learning (ML) systems. Each of the analog memory elements is a micro-dynamical system that is driven by the physics of Fowler-Nordheim (FN) quantum tunneling, whereas the system level learning modulates the state trajectory of the memory ensembles towards the optimal solution. We show that the extrinsic energy required for modulation can be matched to the dynamics of learning and weight decay leading to a significant reduction in the energy-dissipated during ML training. With the energy-dissipation as low as 5 fJ per memory update and a programming resolution up to 14 bits, the proposed synapse array could be used to address the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
On-device Synaptic Memory Consolidation using Fowler-Nordheim Quantum-tunneling
Synaptic memory consolidation has been heralded as one of the key mechanisms for supporting continual learning in neuromorphic Artificial Intelligence (AI) systems. Here we report that a Fowler-Nordheim (FN) quantum-tunn…
Continual LearningHigher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability
We report a higher-order neuromorphic Ising machine that exhibits superior scalability compared to architectures based on quadratization, while also achieving state-of-the-art quality and reliability in solutions with co…
AllCombinatorial OptimizationON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-anneal…
Combinatorial OptimizationNeural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and effic…
Continual LearningReinforcement LearningExperimental Demonstration of Array-level Learning with Phase Change Synaptic Devices
The computational performance of the biological brain has long attracted significant interest and has led to inspirations in operating principles, algorithms, and architectures for computing and signal processing. In thi…