Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware
CoRR(2024)
摘要
A novel high-fan-in differential superconductor neuron structure designed for
ultra-high-performance Spiking Neural Network (SNN) accelerators is presented.
Utilizing a high-fan-in neuron structure allows us to design SNN accelerators
with more synaptic connections, enhancing the overall network capabilities. The
proposed neuron design is based on superconductor electronics fabric,
incorporating multiple superconducting loops, each with two Josephson
Junctions. This arrangement enables each input data branch to have positive and
negative inductive coupling, supporting excitatory and inhibitory synaptic
data. Compatibility with synaptic devices and thresholding operation is
achieved using a single flux quantum (SFQ) pulse-based logic style. The neuron
design, along with ternary synaptic connections, forms the foundation for a
superconductor-based SNN inference. To demonstrate the capabilities of our
design, we train the SNN using snnTorch, augmenting the PyTorch framework.
After pruning, the demonstrated SNN inference achieves an impressive 96.1
accuracy on MNIST images. Notably, the network exhibits a remarkable throughput
of 8.92 GHz while consuming only 1.5 nJ per inference, including the energy
consumption associated with cooling to 4K. These results underscore the
potential of superconductor electronics in developing high-performance and
ultra-energy-efficient neural network accelerator architectures.
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