Weekly Research Newsletter - KIIT

By Priyansi, Junaid Rahim and Biswaroop Bhattacharjee

Weekly Research Newsletter - KIIT

Weekly Research Newsletter - KIIT




Weekly Research Newsletter - KIIT
27th March, 2021
We are excited to share this week’s picks for the research newsletter. We hope you’ll enjoy reading them over the weekend. Both the papers of this issue are from the proceedings of MLSys 2021.
You can find all the papers on this page. Really interesting conference for those interested in ML Engineering and Systems.

Cortex: A Compiler for Recursive Deep Learning Models
By Pratik Fegade, Tianqi Chen, Phillip Gibbons, Todd Mowry
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those found in vendor libraries. This approach often leaves significant performance on the table, especially for the case of recursive deep learning models. In this paper, we present Cortex, a compiler-based approach to generate highly-efficient code for recursive models for low latency inference. Our compiler approach and low reliance on vendor libraries enables us to perform end-to-end optimizations, leading to up to 14X lower inference latencies over past work, across different backends.
To Bridge Neural Network Design and Real-World Performance: A Behaviour Study for Neural Networks
By Xiaohu Tang, Shihao Han, Li Lyna Zhang, Ting Cao, Yunxin Liu
The boom of edge AI applications has spawned a great many neural network (NN) algorithms and inference platforms. Unfortunately, the fast pace of development in their fields have magnified the gaps between them. A well-designed NN algorithm with reduced number of computation operations and memory accesses can easily result in increased inference latency in real-world deployment, due to a mismatch between the algorithm and the features of target platforms. Therefore, it is critical to understand the behaviour characteristics of NN design space on target platforms. However, none of existing NN benchmarking or characterization studies can serve this purpose. They only evaluate some sparse configurations in the design space for the purpose of platform optimization rather than the scaling in every design dimension for NN algorithm efficiency. This paper presents the first empirical study on the NN design space to learn NN behaviour characteristics on different inference platforms. The revealed characteristics can be used as guidelines to design efficient NN algorithms. We profile ten-thousand configurations from a cutting-edge NN design space on seven industrial edge AI platforms. Seven key findings as well as their causes and implications for efficient NN design are highlighted.
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Priyansi, Junaid Rahim and Biswaroop Bhattacharjee

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