Low Power Hardware-In-The-Loop Neuromorphic Training Accelerator
The training process for spiking neural networks can be very computationally intensive. Approaches such as evolutionary algorithms may require evaluating thousands or millions of candidate solutions. In this work, we propose using neuromorphic cores implemented on a Xilinx Zynq system on chip to accelerate and improve the energy efficiency of the evaluation step of an evolutionary training approach. We demonstrate this can significantly reduce the required energy to evolve a network with some cases showing greater than 10 times improvement as compared to a CPU-only system.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1827021
- Resource Relation:
- Conference: International Conference on Neuromorphic Systems (ICONS) 2021 - Knoxville, Tennessee, United States of America - 7/27/2021 5:00:00 AM-7/29/2021 5:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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