FLAM defines aggregatable performance measures for federated learning that match centralized evaluation results without requiring a global test dataset.
Learning Multiple Layers of Features from Tiny Im- ages
2 Pith papers cite this work. Polarity classification is still indexing.
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PoTAcc delivers an end-to-end pipeline and three shift-PE FPGA accelerators for PoT-quantized DNNs that deliver up to 3.6x speedup and 78% energy reduction versus CPU-only runs on PYNQ-Z2 and Kria boards.
citing papers explorer
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FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
FLAM defines aggregatable performance measures for federated learning that match centralized evaluation results without requiring a global test dataset.
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PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
PoTAcc delivers an end-to-end pipeline and three shift-PE FPGA accelerators for PoT-quantized DNNs that deliver up to 3.6x speedup and 78% energy reduction versus CPU-only runs on PYNQ-Z2 and Kria boards.