Difference between revisions of "Developing a Transposition Unit to Accelerate ML Workloads (1-3S/B)"
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Latest revision as of 09:51, 7 November 2022
- Type: Bachelor / Semester Thesis or Group Project
- Professor: Prof. Dr. L. Benini
Transposing matrices is an important operation used in countless applications from scientific computing to machine learning workloads.
At IIS, we are actively developing a DMA engine to accelerate data movement in various of our platforms. We would now create a transposition unit that transposes matrices while they are copied throughout the system. The accelerator should work of full-precision integer and floating point formats for general purpose scientific computing as well as narrow 4bit / 2bit (and even 1bit?) typed for ML interference.
In this project, you develop, implement, and evaluate a flexible transposition unit able to work on various data widths.
- 40% Design and implementation of the unit
- 30% Verification
- 30% Evaluation
- Interest in memory systems
- Experience with digital design in SystemVerilog as taught in VLSI I
- Preferred: Knowledge of AXI4