DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks

• URL: http://arxiv.org/abs/2004.10341v1
• Date: Tue, 21 Apr 2020 23:26:23 GMT
• Title: DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks
• Authors: Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
• Abstract summary: We study the latency and energy of different mapping policies on different DRAM architectures. The results show that the energy-efficient DRAM accesses can be achieved by a mapping policy that orderly prioritizes to maximize the row buffer hits, bank- and subarray-level parallelism.
• Score: 15.115813664357436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been proposed to exploit subarray-level parallelism for decreasing the access latency. Towards this, we present a design space exploration methodology to study the latency and energy of different mapping policies on different DRAM architectures, and identify the pareto-optimal design choices. The results show that the energy-efficient DRAM accesses can be achieved by a mapping policy that orderly prioritizes to maximize the row buffer hits, bank- and subarray-level parallelism.

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