Presentation
IncineRate: Multi-Modal FPGA Accelerator for SCNNs
DescriptionSpiking 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.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
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