Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟯𝟰 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗜𝗧 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗣𝗿𝗼𝗽𝗼𝘀𝗲 ‘𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗢𝗽𝘁𝗶𝗰𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗗𝗢𝗡𝗡)’: 𝗔𝗻 𝗘𝗻𝗲𝗿𝗴𝘆-𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗢𝗽𝘁𝗶𝗰𝗮𝗹 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿 𝗳𝗼𝗿 𝗗𝗲𝗲𝗽-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗽𝘁𝗶𝗰𝗮𝗹 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗳𝗼𝗿 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗙𝗿𝗲𝗲𝗹𝘆 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗿𝗲𝗰𝗼𝗻𝗳𝗴𝘂𝗿𝗮𝗯𝗹𝗲 𝗼𝗽𝘁𝗶𝗰𝗮𝗹 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗳𝗼𝗿 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🔸 This paper is published nature 2021. 🔸 MIT researchers have developed a novel optical DNN (artificial deep neural network) accelerator that uses light to transmit activation and weight data. They called it DONN (digital optical neural network). With only a few percentage points accuracy cost, this system can achieve a transmission energy advantage up 1000x over traditional electronic devices. 🔸 The research was published ‘Freely scalable and reconfigurable optical hardware for deep learning’ in Nature’s Scientific Reports. DONN tackles the problem of power consumption in neural networks by replacing electric currents with optical signals. DONN’s constant energy usage has enabled it to scale up for larger deep learning models while keeping costs low and performance high – a single 8-bit MAC operation only requires 3 femtojoules (fJ) compared to more than 1,000 fJ needed on an electronic chip. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power;  🔸 however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values.  🔸 The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations.  🔸 In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of >10μm. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------