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Seminars

Seminar
FIELD Mathematics
DATE December 07 (Mon), 2020
TIME 10:00-11:00
PLACE Online
SPEAKER Park, Jongse
HOST Keum, JongHae
INSTITUTE KAIST
TITLE Achieving Performance and Programmability in DNN Acceleration Solutions
ABSTRACT As the computational demand of emerging applications (e.g., deep learning) rapidly increases, the benefits of conventional general-purpose solutions are diminishing. Acceleration has been a promising alternative solution that delivers orders-of-magnitude higher performance and energy efficiency gains compared to the general-purpose counterparts. However, achieving both performance and programmability at the same time is still the greatest challenge to facilitate the use of such acceleration solutions.
I will talk about two works that address this challenge by developing hardware-software co-designed full stack solutions. I will first talk about Bit Fusion, a novel DNN acceleration solution, which leverages the inherent algorithmic properties of DNNs and provides a bit-flexible accelerator that dynamically fuse the on-chip computing units to match the bit width of individual DNN layers. I will then talk about INCEPTIONN, a hardware-algorithm co-designed in-network acceleration solution for distributed DNN training system, which significantly reduces the inter-node communication overhead and in turn substantially improves the end-to-end DNN training performance.

(Zoom Link: https://kaist.zoom.us/j/85378884451)
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