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...
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
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
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...
End of the Road for My CAREER,
Workshop on Negative Outcomes, Post-mortems, and Experiences (NOPE) at the 48th Annual IEEE/ACM International Symposium on Microarchitecture, 2015, Sunday, December 6, 2015: