Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures

Lammie, Corey, Eshraghian, Jason K., Li, Chenqi, Amirsoleimani, Amirali, Genov, Roman, Lu, Wei D., and Azghadi, Mostafa Rahimi (2022) Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures. In: Proceedings of the IEEE International Symposium on Circuits and Systems. pp. 1107-1111. From: ISCAS 2022: IEEE International Symposium on Circuits and Systems, 27 May - 1 June 2022, Austin, TX, USA.

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The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL) algorithms, but the degree of impact varies based on algorithmic features. These include network architecture, capacity, weight distribution, and the type of inter-layer connections. Techniques are continuously emerging to efficiently train sparse neural networks, which may have activation sparsity, quantization, and memristive noise. In this paper, we present an extended Design Space Exploration (DSE) methodology to quantify the benefits and limitations of dense and sparse mapping schemes for a variety of network architectures. While sparsity of connectivity promotes less power consumption and is often optimized for extracting localized features, its performance on tiled RRAM arrays may be more susceptible to noise due to under-parameterization, when compared to dense mapping schemes. Moreover, we present a case study quantifying and formalizing the trade-offs of typical non-idealities introduced into l-Transistor-l-Resistor (ITIR) tiled memristive architectures and the size of modular crossbar tiles using the CIFAR-10 dataset.

Item ID: 77679
Item Type: Conference Item (Research - E1)
ISBN: 9781665484855
Keywords: Deep Learning, Design Space Exploration, In-Memory Computing (IMC), Memristor, RRAM
Copyright Information: © 2022 IEEE.
Date Deposited: 01 Mar 2023 00:20
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 100%
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