Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟭𝟬 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗿𝗰𝗵-𝗡𝗲𝘁: A Family Of Neural Networks Built With Operators To Bridge The Gap Between Computer Architecture of ASIC Chips And Neural Network Model Architectures Follow me for a similar post: 🇮🇳 Ashish Patel. ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗔𝗿𝗰𝗵-𝗡𝗲𝘁: 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗔𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 🔸 This Paper is published arxiv 2021. 🔸 The computation power of Deep Neural Networks is a significant challenge to their real-world applications. New developments in the field are outpacing the recent ASICs (Application Specific Integrated Circuit) that have neural network acceleration since ASIC takes several years to develop. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:   🛫 The structure of ArchNet comprises five operators: 3×3 Convolutions, Batch Normalization, Concatenation, 2×2 Max-pooling, and Fully-Connected layers. 🛫 The conversion to Arch-Net is simpler without labeled data as researchers employ Blockwise Model Distillation on feature maps. 🛫 Researchers did extensive experiments on image classification and machine translation tasks to confirm that Arch-Net is effective, efficient, and fast. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Vast requirement of computation power of Deep Neural Networks is a significant hurdle to their real-world applications. Much recent Application Specific Integrated Circuit (ASIC) chips feature dedicated hardware support for Neural Network Acceleration.  🔸 However, as ASICs take multiple years to develop, they are inevitably outpaced by the latest development in Neural Architecture Research. For example, Transformer Networks do not have native support on many popular chips, and hence are difficult to deploy.  🔸 In this Paper, we propose Arch-Net, a family of Neural Networks made up of only operators efficiently supported across most ASIC architectures.  🔸 When an Arch-Net is produced, less common network constructs, like Layer Normalization and Embedding Layers, are eliminated progressively through label-free Blockwise Model Distillation while performing sub-eight bit quantization simultaneously maximize performance.  🔸 Empirical results on machine translation and image classification tasks confirm that we can transform the latest developed Neural Architectures into fast running and as-accurate Arch-Net, ready for deployment on multiple mass-produced ASIC chips.  #computervision #artificialintelligence #deeplearning