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Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service
In this paper we show hyperdimensional computing can be a rescue for privacy-preserving machine learning over encrypted data.
CSQ: Centered Symmetric Quantization for Extremely Low Bit Neural Networks
We show that this asymmetry in the number of positive and negative quantization levels can result in significant quantization error and performance degradation at low precision.
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks
Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision.
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation
We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption.
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