Prof. Emeritus David Patterson is profiled in a CNBC article which describes how he postponed retirement to conduct research at Google into the Tensor Processing Unit (TPU), an ambitious new chip that's designed to run at least 10 times faster than today's processors and is sophisticated enough to handle the intensive computations required for artificial intelligence.  Without it, it is estimated that Google would have to double its data centers to support even a limited amount of voice processing.  Prof. Patterson described his work on the TPU when he returned to Berkeley as a Colloquium speaker on May 3rd.

U.C. Berkeley has the top ten most AMiner Most Influential Scholar Award winners across all fields of computer science in 2016 and the top five most award winners in the fields of Computer Vision, Database, Machine Learning, Multimedia, Security, Computer Networking, and System.  The 28 CS faculty members included in the rankings were among the 100 most-cited authors in 12 of the 15 research areas evaluated. Two were among the 100 most-cited authors in 3 different areas each: Scott Shenker ranked #1 in Computer Networking, #51 in System, and #99 in Theory; and Trevor Darrell ranked #8 in Mulitmedia, #18 in Computer Vision, and #100 in Machine Learning.  Out of the 700,000 researchers indexed, only 16 appeared on three or more area top 100 lists.  See a more detailed breakdown of our influential faculty scholars.

CS undergraduates Tomás Vega Gálvez and Corten Singer have been chosen the $10,000 Lemelson-MIT “Drive it!” Undergraduate Team Winner for an open-source smart add-on system for wheelchairs. Vega and Singer created WheelSense, a modular, customizable add-on system for wheelchairs that provides spatial awareness for visually impaired users to identify obstacles and ease their navigation. It has three features: frontal staircase detection through auditory feedback, backward obstacle-avoidance assistance through auditory feedback, and lateral ramp-edge detection through haptic feedback. They hope to disrupt the expensive market for assistive technologies for the disabled community by making their technology open source.  The “Drive it!” Lemelson-MIT Student Prize rewards students working on technology-based inventions that can improve transportation.

EECS Chair and CS Prof. Jitendra Malik will discuss Deep Visual Understanding from Deep Learning as one of the keynote speakers at the Embedded Vision Summit on May 2, 2017.  The summit is the only event focused exclusively on the technologies, hardware, and software that bring visual intelligence to products.  This year, "It's all about deployable computer vision and deep learning" and will feature more than 90 expert presenters in 4 conference tracks over three days.

CS Prof. Emeritus David Patterson co-authored and presented a report on Tensor Processing Units (TPUs) at a regional seminar of the National Academy of Engineering, held at the Computer History Museum in Menlo Park on April 5, 2017.   TPUs, which have been deployed in Google datacenters since 2015, are printed-circuit cards which are inserted into existing servers and act as co-processors tailored for neural-network calculations.  Prof. Patterson says that TPUs are "an order of magnitude faster than contemporary CPUs and GPUs" with an even larger relative performance per watt.  According to an article for the IEEE Spectrum, TPUs are "built for doing inference," having hardware that operates on 8-bit integers rather than the higher-precision floating-point numbers used in CPUs and GPUs.

CS Prof. Michael Jordan, graduate students Philipp Moritz and Robert Nishihara, and research in the RISELab are featured in a Datanami article titled "Meet Ray, the Real-Time Machine-Learning Replacement for Spark."  Ray is one of the first technologies to emerge from RISELab, the successor to AMPLab and its host of influential distributed technologies including Spark, Mesos, and Tachyon. Ray is a new distributed framework designed to enable Python-based machine learning and deep learning workloads to execute in real-time with MPI-like power and granularity. This framework is ostensibly a replacement for Spark, which is seen as too slow for some real-world AI applications.

The paper “X-Trace: A Pervasive Network Tracing Framework”, authored by EECS alumi Rodrigo Fonseca (Ph.D. ’08) and George Porter (Ph.D. ’08) and Professors Randy Katz, Scott Shenker, and Ion Stoica, has received the 2017 Networked Systems Design and Implementation (NSDI) Test-of-Time Award. X-Trace was not the first tracing framework, but it was influential given that it was effectively the first framework for end-to-end tracing to focus on generality and pervasiveness. The researchers implemented X-Trace in protocols and software systems, and in their prize-winning paper, they set out to explain three different use scenarios: domain name system (DNS) resolution; a three-tiered photo-hosting website; and a service accessed through an overlay network.

Postdoctoral researcher Matthias Vallentin is developing VAST,  a  forensic analysis tool  designed to help prioritize the investigation of computer security breaches.  It complements Bro, a security tool  devised by Prof. Vern Paxson when he was a graduate student 22 years ago and which is now used worldwide, to instantly collect huge volumes of log data that a hack might compromise.  “Maybe the external machine also appeared in a phishing email, which contained a PDF attachment. Not only that, but the PDF also includes a malicious payload, which upon opening, sends sensitive information from the employee’s computer to a cyber criminal.  VAST supports this iterative process to reconstruct the complete picture and presents it on a platter” explains Vallentin.  The function, development, and industrial potential of these tools are discussed in a Berkeley Research article.

CS graduate student Baiyu Chen (advisor: Alexei Efros) and Anthony Barrs were awarded top prize and $50,000 for their design at the Infrastructure Vision 2050 Challenge.  Their idea, profiled in an article for Fortune, was to construct a "Hyperlane," or a single platform the size of four interstate lanes that would run parallel to pre-existing highways in order for self-driving cars to travel at high speeds with no chance of getting into a jam.

Tomás Vega, a senior computer science and cognitive science major, was part of a team that participated in the first collegiate Tikkun Olam Makers (TOM) event and which was profiled in a Jewish News article titled "Cal students pull a marathon to engineer disability solutions."  His team, led by Berkeley alumnus Pierluigi Mantovani, designed a special pair of gloves to help a Berkeley filmmaker with very limited use of his hands navigate a computer screen.  The filmmaker, who had to use a joystick with his lower lip to navigate, can now perform the same task by just slightly moving his wrists.  The gloves use electromyography, which detects signals from his muscles.   “When someone tells you, ‘Thank you for changing my life, for improving the quality of my life,’ there’s nothing like that,” Vega said.