Select Talks

Here are a list of some recent talks that I want to share with the rest of the world. Enjoy!

PRESENTATIONS

Tiny Robot Learning: Tiny Machine Learning (tinyML) for Robotics, at Conference on Robot Learning (2021), Keynote talk., Wednesday, November 10, 2021:
Tiny machine learning (tinyML) is a fast-growing and emerging field at the intersection of machine learning (ML) algorithms and low-cost embedded systems. It enables on-device analysis of sensor data (vision, audio, IMU, etc.) at ultra-low-power consumption (<1mW). Moving machine learning compute close to the sensor(s) allows for an expansive new variety of always-on ML use-cases, especially in size, weight and power (SWaP) constrained robots. This talk introduces the broad vision behind tinyML, and specifically, it focuses on exciting new applications that tinyML enables for cheap and... Read more about Tiny Robot Learning: Tiny Machine Learning (tinyML) for Robotics
TinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems, at TinyML Summit 2020 (http://tinyml.org), Thursday, February 13, 2020:

Tiny machine learning (ML) is poised to drive enormous growth within the IoT hardware and software industry. Measuring the performance of these rapidly proliferating systems, and comparing them in a meaningful way presents a considerable challenge; the complexity and dynamicity of the field obscure the measurement of progress and make embedded ML application and system design and deployment intractable. To foster more systematic development, while enabling innovation, a fair, replicable, and robust method of evaluating tinyML systems is required. A reliable and widely accepted tinyML...

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Gables: A Roofline Model for Mobile SoCs, at Workshop on Infrastructure and Methodology for SoC-level Performance and Power Modeling (co-located with ASPLOS 2019), Saturday, April 13, 2019:

Over a billion mobile consumer system-on-chip (SoC) chipsets ship each year. Of these, the mobile consumer market undoubtedly involving smartphones has a significant market share. Most modern smartphones comprise of advanced SoC architectures that are made up of multiple cores, GPS, and many different programmable and fixed-function accelerators connected via a complex hierarchy of interconnects with the goal of running a dozen or more critical software usecases under strict power, thermal and energy constraints. The steadily growing complexity of a modern SoC challenges hardware...

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Ten Commandments for Mobile Computing, at Infrastructure and Methodology for SoC-level Performance and Power Modeling (co-located with ASPLOS 2019), Saturday, April 13, 2019:
Mobile computing has grown drastically over the past decade. Despite the rapid pace of advancements, mobile device understanding, benchmarking, and evaluation are still in their infancies, both in industry and academia. This article presents an industry perspective on the challenges facing mobile computer architecture, specifically involving mobile workloads, benchmarking, and experimental methodology, with the hope of fostering new research within the community to address pending problems. These challenges pose a threat to the systematic development of future mobile systems, which, if... Read more about Ten Commandments for Mobile Computing
Mobile Robotics for Computer Architects, at International Workshop on Domain Specific System Architecture (DOSSA), Sunday, October 21, 2018:
Autonomous computing systems are marching toward ubiquity in everyday life. In recent years, Unmanned Aerial Systems (UAS) have seen an influx of attention, specifically in application areas with a strong demand for autonomy. A key challenge in making mobile robots such as UAS autonomous is their need to operate under power and energy constraints, which severely limit their onboard sensing, intelligence, and endurance capabilities. To overcome these challenges, researchers must understand how endurance, power efficiency, and computational bottlenecks in autonomous systems relate to one... Read more about Mobile Robotics for Computer Architects
The Vision Behind MLPerf: A Broad ML Benchmark Suite for Measuring the Performance of ML Software Frameworks, ML Hardware Accelerators, and ML Cloud and Edge Platforms, at Samsung Technology Forum in Austin at Samsung Austin Research Center (SARC), Tuesday, October 16, 2018:

Deep Learning is transforming the field of machine learning (ML) from theory to practice. It has also sparked a renaissance in computer system design, fueled by the industry’s need to improve ML accuracy and performance rapidly. But despite the fast pace of innovation, there is a key issue affecting the industry at large, and that is how to enable fair and useful benchmarking of ML software frameworks, ML hardware accelerators and ML platforms. There is a need for systematic ML benchmarking that is both representative of real-world use-cases, and useful for fair comparisons...

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