An FPGA-Based Hardware Emulator for Neuromorphic Chip With RRAM

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An FPGA-Based Hardware Emulator for Neuromorphic Chip With RRAM
Title:
An FPGA-Based Hardware Emulator for Neuromorphic Chip With RRAM
Journal Title:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publication Date:
25 December 2018
Citation:
T. Luo et al., "An FPGA-Based Hardware Emulator for Neuromorphic Chip With RRAM," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 2, pp. 438-450, Feb. 2020, doi: 10.1109/TCAD.2018.2889670.
Abstract:
Neuromorphic chip with RRAM devices has been demonstrated as a promising computing platform for neural network-based applications. By directly mapping the weight matrices of neural networks onto RRAM-based crossbar arrays, high energy, and area efficiency can be achieved. However, the design of an RRAM-based neuromorphic chip faces many constraints due to the variability and limitations of RRAM. Simulation and emulation can help in the design of a neuromorphic chip prior to fabrication. However, software-based chip simulation on CPU is slow, especially for large-scale network-on-chip (NoC)-based chip design. In this paper, we present a hardware emulator on field-programmable gate array (FPGA) for an RRAM-based neuromorphic chip. Our emulator supports the emulation of static and dynamic variation of the RRAMbased crossbars used in the neural cores of a neuromorphic chip. Furthermore, an NoC is also implemented on FPGA to emulate the communication between the neural cores. Using the emulator, we show that effects, such as RRAM write and read noise and stuck-at faults affect the accuracy of an application on a neuromorphic chip. We also demonstrate the utility of the emulator in investigating NoC topologies, routing buffer depths, and neural core mappings.
License type:
PublisherCopyrights
Funding Info:
This work was supported by the Singapore Government’s Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering domain) under Grant A1687b0033.
Description:
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0278-0070
1937-4151
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