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Title: LRMP: Layer Replication with Mixed Precision for spatial in-memory DNN accelerators
In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a promising approach to address the rapidly growing computational demands of Deep Neural Networks (DNNs). Mapping DNN layers spatially onto NVM-based IMC accelerators achieves high degrees of parallelism. However, two challenges that arise in this approach are the highly non-uniform distribution of layer processing times and high area requirements. We propose LRMP, a method to jointly apply layer replication and mixed precision quantization to improve the performance of DNNs when mapped to area-constrained IMC accelerators. LRMP uses a combination of reinforcement learning and mixed integer linear programming to search the replication-quantization design space using a model that is closely informed by the target hardware architecture. Across five DNN benchmarks, LRMP achieves 2.6–9.3× latency and 8–18× throughput improvement at minimal (<1%) degradation in accuracy.  more » « less
Award ID(s):
2107011
PAR ID:
10663209
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Frontiers in Artificial Intelligence
Date Published:
Journal Name:
Frontiers in Artificial Intelligence
Volume:
7
ISSN:
2624-8212
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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