Breaking the Memory Wall for AI Chip with a New Dimension

TL;DR

This paper introduces Sunrise, a 3D AI chip with near-memory computing architecture that significantly improves memory bandwidth, energy efficiency, and capacity to support advanced AI models beyond current limitations.

Contribution

The paper presents a novel 3D AI chip architecture with near-memory computing, achieving substantial improvements in performance, energy efficiency, and memory capacity over existing chips.

Findings

Achieves comparable energy efficiency on 40nm technology as competitors on 7nm.

Projected over tenfold increase in energy efficiency with advanced technology.

Sevenfold performance improvement and twentyfold memory capacity increase.

Abstract

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.