GPU-RANC: A CUDA Accelerated Simulation Framework for Neuromorphic Architectures
Sahil Hassan, Michael Inouye, Miguel C. Gonzalez, Ilkin Aliyev, Joshua, Mack, Maisha Hafiz, Ali Akoglu

TL;DR
This paper introduces a GPU-accelerated version of the RANC simulation framework, significantly speeding up neuromorphic architecture studies and enabling more efficient design space exploration for SNNs.
Contribution
The paper presents a CUDA-based implementation of RANC, achieving up to 780x speedup over the serial version for neuromorphic simulations.
Findings
Up to 780x speedup in simulation time.
Effective parallelization approach demonstrated.
Enhanced feasibility for neuromorphic architecture research.
Abstract
Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable Architecture for Neuromorphic Computing (RANC) is one such tool that offers ability to execute pre-trained Spiking Neural Network (SNN) models within a unified ecosystem through both software-based simulation and FPGA-based emulation. RANC has been utilized by the community with its flexible and highly parameterized design to study implementation bottlenecks, tune architectural parameters or modify neuron behavior based on application insights and study the trade space on hardware performance and network accuracy. In designing architectures for use in neuromorphic computing, there are an incredibly large number of configuration parameters such as number and…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Parallel Computing and Optimization Techniques
