Hassan Kianinejad

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Co-Authors

Gregory Diamos

Baidu (China)

Sharan Narang

Google (United States)

Heewoo Jun

OpenAI (United States)

Yanqi Zhou

Ningbo City College of Vocational Technology

Newsha Ardalani

Baidu (China)

Joel Hestness

Cerebras Systems (United States)

Md. Mostofa Ali Patwary

University of Bergen

Yang Yang

Hefei Normal University

Kenneth Church

Northeastern University

Zhenman Fang

Simon Fraser University

Cooperative Institutions

Baidu (China)

Intel (United States)

Silicon Valley University

Google (United States)

University of California, Los Angeles

Peking University

University of Wisconsin–Madison

Universidad del Noreste

University of California System

Beijing University of Posts and Telecommunications

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Revisiting FPGA Acceleration of Molecular Dynamics Simulation with Dynamic Data Flow Behavior in High-Level Synthesis

arXiv (Cornell University) (2016)

Jason Cong Zhenman Fang Hassan Kianinejad Peng Wei

Molecular dynamics (MD) simulation is one of the past decade's most important tools for enabling biology scientists and researchers to explore human health and diseases. However, due to the computation complexity of the MD algorithm, it takes weeks or even months to simulate a comparatively simple biology entity on conventional multicore processors. The critical path in molecular dynamics simulations is the force calculation between particles inside the simulated environment, which has abundant parallelism. Among various acceleration platforms, FPGA is an attractive alternative because of its low power and high energy efficiency. However, due to its high programming cost using RTL, none of the mainstream MD software packages has yet adopted FPGA for acceleration. In this paper we revisit the FPGA acceleration of MD in high-level synthesis (HLS) so as to provide affordable programming cost. Our experience with the MD acceleration demonstrates that HLS optimizations such as loop pipelining, module duplication and memory partitioning are essential to improve the performance, achieving a speedup of 9.5X compared to a 12-core CPU. More importantly, we observe that even the fully optimized HLS design can still be 2X slower than the reference RTL architecture due to the common dynamic (conditional) data flow behavior that is not yet supported by current HLS tools. To support such behavior, we further customize an array of processing elements together with a data-driven streaming network through a common RTL template, and fully automate the design flow. Our final experimental results demonstrate a 19.4X performance speedup and 39X energy efficiency for the widely used ApoA1 MD benchmark on the Convey HC1ex FPGA compared to a 12-core Intel Xeon server.

Dynamics

Dynamic simulation

10.48550/arxiv.1611.04474

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A Proposed Hierarchy of Deep Learning Tasks

Joel Hestness Sharan Narang Newsha Ardalani Heewoo Jun Hassan Kianinejad

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Deep Learning Scaling is Predictable, Empirically

arXiv (Cornell University) (2017)

Joel Hestness Sharan Narang Newsha Ardalani Gregory Diamos Heewoo Jun

Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would like a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements to advance the state-of-the-art. This paper presents a large scale empirical characterization of generalization error and model size growth as training sets grow. We introduce a methodology for this measurement and test four machine learning domains: machine translation, language modeling, image processing, and speech recognition. Our empirical results show power-law generalization error scaling across a breadth of factors, resulting in power-law exponents---the "steepness" of the learning curve---yet to be explained by theoretical work. Further, model improvements only shift the error but do not appear to affect the power-law exponent. We also show that model size scales sublinearly with data size. These scaling relationships have significant implications on deep learning research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and underscore the importance of continued computational scaling.

10.48550/arxiv.1712.00409

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Citations (358)