[CMU AI Seminar] September 27 at 12pm (NSH 3305 & Zoom) -- Jian Zhang (SambaNova Systems) -- MLSys Innovations Beyond the Hardware Goldilocks Zone -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

This is happening now in NSH 3305 (there's pizza).

> On Sep 25, 2022, at 5:03 PM, Asher Trockman <ashert at cs.cmu.edu> wrote:
> 
> 
> Dear all,
> 
> We look forward to seeing you this Tuesday (9/27) from 12:00-1:00 PM (U.S. Eastern time) for the next talk of this semester's CMU AI Seminar, sponsored by SambaNova Systems. The seminar will be held in NSH 3305 with pizza provided and will be streamed on Zoom.
> 
> To learn more about the seminar series or to see the future schedule, please visit the seminar website.
> 
> On 9/27, Jian Zhang (SambaNova Systems) will be giving a talk titled "MLSys Innovations Beyond the Hardware Goldilocks Zone" to share SambaNova's recent advances in software/hardware co-design for sparse training (e.g., of large language models).
> 
> Title: MLSys Innovations Beyond the Hardware Goldilocks Zone
> 
> Talk Abstract: The Goldilocks zone, or habitable zone, limits life to a restricted part of the solar system. In a similar way, the limitations of conventional hardware impose a Goldilocks zone for ML system innovations. In recent years, the emerging deep learning accelerators have launched an unprecedented opportunity for ML systems advance at different layers of the software and machine learning stacks. In this tech talk, I am very excited to share a case study for building large scale models on the reconfigurable dataflow units (RDU) at SambaNova Systems. At the ML application layer, I want to highlight the 0-1 accuracy breakthrough in High Resolution 3D segmentation enabled by the large memory capacity in RDU. In the layer of ML training algorithms, I will unbox our recent advance in SW/HW codesign for RDU sparse training; this endeavor leads to a 6X faster time-to-accuracy for pretraining large language models over standard dense training on A100 GPUs. Lastly, I will showcase how our team built the prototype of learned RDU performance optimization methods, which pushes further towards fully unleashing the hardware capability of RDU with significantly reduced engineering cost than rule-based methods. We hope that the emerging accelerators will trigger many more ML system innovations from the community.
> 
> Speaker Bio: Jian Zhang is the Director of Machine Learning at SambaNova Systems. He leads the ML team which builds the deep learning foundations for SambaNova’s large-scale enterprise AI solutions. With the mission of democratizing modern foundation model systems, the ML team at SambaNova innovates on both the machine learning and the system aspects, including productionalizing large foundation models and ML/hardware co-design on emerging hardware. Before joining SambaNova Systems, Jian got his PhD in machine learning from Stanford University focusing on machine learning and natural language processing systems.
> 
> In person: NSH 3305
> Zoom Link:  https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
> 
> Thanks,
> Asher Trockman
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