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Found 3 papers, 1 papers with code
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip.
Approximate Computing and the Efficient Machine Learning Expedition
In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems.
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance
We therefore propose HERO, a Hessian-enhanced robust optimization method, to minimize the Hessian eigenvalues through a gradient-based training process, simultaneously improving the generalization and quantization performance.
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