FPGA hardware for event-graph NN achieves 92.7% accuracy on SHD dataset with fewer parameters than SOTA while outperforming prior FPGA SNNs.
In: 2022 IEEE Custom Integrated Circuits Conference (CICC)
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Review of CMOS compatibility advantages and challenges for semiconductor spin qubits aimed at enabling large-scale fault-tolerant quantum computing.
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Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
FPGA hardware for event-graph NN achieves 92.7% accuracy on SHD dataset with fewer parameters than SOTA while outperforming prior FPGA SNNs.
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CMOS compatibility of semiconductor spin qubits
Review of CMOS compatibility advantages and challenges for semiconductor spin qubits aimed at enabling large-scale fault-tolerant quantum computing.