학술논문

A 192-Gb 12-High 896-GB/s HBM3 DRAM with a TSV Auto-Calibration Scheme and Machine-Learning-Based Layout Optimization
Document Type
Conference
Source
2022 IEEE International Solid-State Circuits Conference (ISSCC) Solid-State Circuits Conference (ISSCC), 2022 IEEE International. 65:444-446 Feb, 2022
Subject
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Machine learning algorithms
Memory management
Layout
Random access memory
Machine learning
Bandwidth
Supercomputers
Language
ISSN
2376-8606
Abstract
Ever since the introduction of high bandwidth memory (HBM DRAM) and its succeeding line-ups, HBM DRAM has been heralded as a prominent solution to tackle the memory wall problem. However, despite continual memory advancements the advent of high-end systems, including supercomputers, hyper-scale data centers and machine learning accelerators, are expediting requirements for higher-performance memory solutions. To accommodate the increasing system-level demands, we introduce HBM3 DRAM, which employs multiple new features and design schemes. Techniques such as an on-die ECC engine, internal NN-DFE I/O signaling, TSV auto-calibration, and layout optimization based on machine-learning algorithms are implemented to efficiently control timing skew margins and SI degradation trade-offs. Furthermore, reduced voltage swings allow for improved memory bandwidth, density, power efficiency and reliability.