Spatial Accelerator Generation and Optimization for Tensor Applications
Name
zhang-zhangzk-sm-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
1.41 MB
Format
Adobe PDF
Checksum (MD5)
102cf274d75eb933909ce558c1835ef6
Author(s)
Zhang, Zhekai
Advisor(s)
Han, Song
Date Issued
September 2023
Publisher
Massachusetts Institute of Technology
Abstract
Modern foundation models and generative AI applications require multiple input modalities (both vision and language), which increases the demand for flexible accelerator architecture.
Existing frameworks suffer from the trade-off between design flexibility and productivity of RTL generation: either limited to very few hand-written templates or cannot automatically generate the RTL.
To address this challenge, we propose the LEGO framework, which automatically generates and optimizes spatial architecture design in the front end and outputs synthesizable RTL code in the back end without RTL templates. LEGO front end finds all possible interconnections between function units and determines the memory system shape by solving the integer linear equations, and establishes the connections by a minimum-spanning-tree-based algorithm and a breadth-first-search-based heuristic algorithm for merging different spatial dataflow designs. LEGO back end then translates the hardware in a primitive-level graph to perform lower-level optimizations, and applies a set of linear-programming
algorithms to optimally insert pipeline registers and reduce the overhead of unused logic when switching spatial dataflows.
Our evaluation demonstrates that LEGO can achieve 3.2× speedup and 2.4× energy efficiency compared to previous work Gemmini, and can generate one architecture for diverse modern foundation models in generative AI applications.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
