Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Accelerator

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

This work proposes the Deep Mapper, as a new near-sensor resistive accelerator architecture for Deep Neural Networks (DNN) inference that co-integrates the sensing and computing phases of resource-constrained edge devices. Deep Mapper is developed to intrinsically realize highly parallelized multi-channel processing of input frames supported by a new dense hardware-friendly mapping methodology. Our circuit-to-application simulation results on the DNN acceleration task show that Deep Mapper reaches an efficiency of 4.71 TOp/s/W outperforming state-of-the-art near-/in-sensor accelerators.

Identifier

85184829382 (Scopus)

ISBN

[9798350382044]

Publication Title

2023 IEEE International Conference on Rebooting Computing Icrc 2023

External Full Text Location

https://doi.org/10.1109/ICRC60800.2023.10386958

Grant

2216772

Fund Ref

National Science Foundation

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