A Review of Recent Advances of Binary Neural Networks for Edge Computing
원문 초록
Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This paper reviews recent advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.
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Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network Latency
First Steps Towards Voice Anonymization for Code-Switching Speech
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory Computing
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