2020/09/28 by Eugene Tam, Tam, Eugene, Shenfei Jiang +19
Computer Science · Engineering · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Ferroelectric and Negative Capacitance Devices #Hardware Architecture (cs.AR) #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Semiconductor materials and devices #cs.AI #cs.AR #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.13664
arxiv created 2020/09/28 · openalex publication_date 2020/09/28 · arxiv updated 2020/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent advancements in deep learning have led to the widespread adoption of artificial intelligence (AI) in applications such as computer vision and natural language processing. As neural networks become deeper and larger, AI modeling demands outstrip the capabilities of conventional chip architectures. Memory bandwidth falls behind processing power. Energy consumption comes to dominate the total cost of ownership. Currently, memory capacity is insufficient to support the most advanced NLP models. In this work, we present a 3D AI chip, called Sunrise, with near-memory computing architecture to address these three challenges. This distributed, near-memory computing architecture allows us to tear down the performance-limiting memory wall with an abundance of data bandwidth. We achieve the same level of energy efficiency on 40nm technology as competing chips on 7nm technology. By moving to similar technologies as other AI chips, we project to achieve more than ten times the energy efficiency, seven times the performance of the current state-of-the-art chips, and twenty times of memory capacity as compared with the best chip in each benchmark.