Seminar: ACRC

ECE Women Community

Bringing ML to the extreme edge: a story of co-optimizing processor architectures, scheduling and models

Date: January,19,2022 Start Time: 10:00 - 11:00 Add to:
Lecturer: Prof. Marian Verhelst
Affiliations: KU Leuven, Belgium

Deep neural network inference comes with significant computational complexity, making their execution until recently only feasible on power-hungry server or GPU platforms. The recent trend towards real-time embedded neural network processing on edge and extreme edge devices requires a thorough cross-layer optimization. The talk will analyze what impacts NN execution energy and latency. Subsequently, we will present different research lines of Prof. Verhelst’s lab exploiting and jointly optimizing NPU/TPU processor architectures, dataflow schedulers and conditional, quantized neural network models for minimum latency and maximum energy efficiency. This includes precision-scalable fully-digital designs, as well as compute-in-memory processors. Finally, this talk will make a case for more methodological design space exploration in the vast optimization space of embedded NN processors, using the ZigZag framework.

 

 

Registration:  https://acrc.net.technion.ac.il/registration-marian-verhelst/

 

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