The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Björn A. Lindqvist (KTH Royal Institute of Technology), Artur Podobas (KTH Royal Institute of Technology)
Supervisor:
Abstract: Spiking neural networks (SNNs) are a promising alternative to conventional artificial neural networks (ANNs) due to their biological interpretability and capability to exploit sparse computation. Specialized hardware for SNNs has advantages over general-purpose devices in terms of power and performance. However, the computational requirements of modern spiking convolutional neural networks (SCNNs) render most SNN hardware inefficient for SCNN acceleration. Therefore, we present IncineRate, a flexible FPGA-based SCNN accelerator architecture. IncineRate has built-in support for many SCNNs, such as AlexNet, VGG16, and ResNets, and can be extended to support other network models. The number of simulation time steps, the network architecture, and other settings are specified at run time, allowing an already deployed device to execute multiple networks without reconfiguration. Our results show that IncineRate achieves state-of-the-art classification accuracy among FPGA-based SCNNs on CIFAR10 and CIFAR100.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF