Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.

ABSTRACT: Analog in-memory computing-a promising approach for energy-efficient acceleration of deep learning workloads-computes matrix-vector multiplications but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable inference accuracy. Here, we develop an hardware-aware retraining approach to systematically examine the accuracy of analog in-memory computing across multiple network topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a realistic crossbar model, we improve significantly on earlier retraining approaches. We show that many larger-scale deep neural networks-including convnets, recurrent networks, and transformers-can in fact be successfully retrained to show iso-ac

SUBMITTER: Rasch MJ 

PROVIDER: S-EPMC10469175 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature