WebPhoneBit provides a set of operator-level optimizations including locality-friendly data layout, bit packing with vectorization and layers integration for efficient binary convolution. We also provide a detailed implementation and parallelization optimization for PhoneBit to optimally utilize the memory bandwidth and computing power of mobile GPUs. http://audiobit.rocks/
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WebPhonebit: efficient gpu-accelerated binary neural net-work inference engine for mobile phones. In DATE, 2024. [Qin et al., 2024a] Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, and Nicu Sebe. Binary neural net-works: A survey. Pattern Recognition, 2024. WebApr 30, 2015 · Mobile devices have wide functionality nowadays. The functionality of these devices is expected to be improved and expanded. However, to be able to accomplish this, power dissipation in the form of... WebOver the last years, a great success of deep neural networks (DNNs) has been witnessed in computer vision and other fields. However, performance and power constraints make it still challenging to deploy DNNs on mobile devices due to their high computational complexity. Binary neural networks (BNNs) have been demonstrated as a promising solution to … the pink door restaurant seattle washington