DTAI Projects
FWO: Efficient microprocessor design using machine learning
Period: 01-2008 → 12-2011
Subgroup: ml
Type: project
Members:
Microprocessor design is a very time-consuming and complex task. The question the designer needs to address is how to use hundreds of millions of transistors to yield maximum performance for a given power budget, temperature budget, chip area budget, etc. This involves exploring a huge space of possible designs. The goal of this research project is to drastically reduce the time spent during the microprocessor design space exploration. Current approaches use a brute force approach that requires many simulations to be run on a cluster of machines. In this project, we propose a more fundamental approach, based on machine learning, that computer architects can use to drive the design space exploration. The end goal is a faster and more efficient design space exploration methodology.
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