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Found 5 papers, 2 papers with code
DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency.
Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity
Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance.
A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding
However, current accelerators for SNN cannot well support the emerging encoding schemes.
E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators.
BLUnet: Arithmetic-free Inference with Bit-serialised Table Lookup Operation for Efficient Deep Neural Networks
In this paper, we propose BLUnet, a table lookup-based DNN model with bit-serialized input to overcome this challenge.
