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Electrical and Computer Engineering
Computer Science
Dean of Engineering

ECE Seminar Series:

Tuesday, March 14, 2017
4:00 PM  to 5:00 PM

1064  Duncan Hall
Rice University
6100 Main St
Houston, Texas, USA

Machine learning (ML) algorithms are increasingly pervasive in tackling the data deluge of the 21st Century. Current ML systems adopt either a centralized cloud computing or a distributed mobile computing paradigm. In both paradigms, the challenge of energy efficiency is drawing increased attention. In cloud computing, data transfer due to inter-chip, inter-board, inter-shelf and inter-rack communications (I/O interface) within data centers is one of the dominant energy costs. This will only intensify with the growing demand for increased I/O bandwidth for high-performance computing in data centers. On the other hand, in mobile computing, energy efficiency is the primary design challenge, as mobile devices have limited energy, computation and storage resources. This challenge is being exacerbated by the need to imbed ML algorithms for enabling on-device inference capabilities. In this talk, I will present holistic system-to-circuit approaches for addressing these energy efficiency challenges. First, I will describe the design of a 4-bit, 4 GS/s bit-error-rate optimal analog-to-digital converter in 90nm CMOS and its use in realizing an energy-efficient 4Gb/s serial link receiver for I/O interface in data centers. Next, I will describe two techniques that can potentially enable on-device deployment of convolutional neural networks (CNNs) by significantly reducing the energy consumption via algorithmic/architectural innovation. Finally, I will identify future research directions in the emerging area of machine learning on resource-constrained silicon platforms.

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