Analog Accelerator For AI/ML Training Workloads Using Stochastic Gradient Descent (Imperial College London)


A new technical paper titled "Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent" was published by researchers at Imperial College London. Abstract "The rapid proliferation of AI models, coupled with growing demand for edge deployment, necessitates the development of AI hardware that is both high-performance and energy-efficient. In this paper, w... » read more

Enabling Training of Neural Networks on Noisy Hardware


Abstract:  "Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog hardware composed of resistive device arrays with non-symmetric conductance modulation characteristics. Recently we proposed a new algorithm, the Tiki-Taka algorithm, that overcomes t... » read more