A case study showing how knowledge distillation, pruning, and quantization let a small LSTM run on a low-end FPGA, with three split configurations trading off latency, power, and resource usage.
Unleashing the tiger: Inference attacks on split learning,
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Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
A case study showing how knowledge distillation, pruning, and quantization let a small LSTM run on a low-end FPGA, with three split configurations trading off latency, power, and resource usage.