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Found 4 papers, 1 papers with code
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference
Over 46 FPGA-design experiments involving eight configurations and four data sets reveal that lightweight neural networks with a flexible $k$ value (dubbed FLightNNs) fully utilize the hardware resources on Field Programmable Gate Arrays (FPGAs), our experimental results show that FLightNNs can achieve 2$\times$ speedup when compared to lightweight NNs with $k=2$, with only 0. 1\% accuracy degradation.
Regularizing Activation Distribution for Training Binarized Deep Networks
Binarized Neural Networks (BNNs) can significantly reduce the inference latency and energy consumption in resource-constrained devices due to their pure-logical computation and fewer memory accesses.
Differentiable Training for Hardware Efficient LightNNs
To reduce runtime and resource utilization of Deep Neural Networks (DNNs) on customized hardware, LightNN has been proposed by constraining the weights of DNNs to be a sum of a limited number (denoted as $k\in\{1, 2\}$) of powers of 2.
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks
For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs.
