Dr. Jishen Zhao

Today's emerging neural computing substrates have their origins in either biology (spiking neural networks, like IBM's TrueNorth), or machine learning (deep convolutional networks, like Google's TPU). There are glaring gaps between these two approaches, since they differ dramatically in terms of architecture, efficiency, usability, and practical applicability to real-world tasks. Deep neural networks, which are based on the nonlinear perceptron neuron model, have recently emerged as a very...

Energy and power are the main constraints in computer architecture today and in the foreseeable future. Unfortunately, there is no silver-bullet technique that can single-handedly solve the problem—-certainly not in computer architecture. In architecture, we need to apply a set of technologies that, in combination, can improve energy efficiently substantially. This talk presents some of these technologies. Cores need to flexibly operate at a range of voltages, and techniques for efficient...

Visual object detection and recognition are needed for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy or latency concerns. In this talk, we will describe how joint algorithm and hardware design can be used to reduce the energy consumption of object detection and recognition while delivering real-time and robust...

In the quest for more intelligent consumer devices, machine learning lets appliances understand what is happening around the computer and what is asked of it, while big data provides the history and context of the environment. But devices must also react to be useful, and for many applications the reaction needs to happen on human timescale to be valuable. For example, an advertisement beacon must beam a discount coupon to the shopper's cellphone in a few hundred milliseconds or the shopper...

Computers used as datacenter servers have usage patterns that differ substantially from those of desktop or laptop computers. We discuss four key differences in usage and their first-order implications for designing computers that are particularly well-suited as servers: data movement, thousands of transactions per second, program isolation, and measurement underpinnings. Maintaining high-bandwidth data movement requires coordinated design decisions throughout the memory system,...

Current-day data centers and IaaS clouds (e.g. Amazon EC2, MS Azure, Google GCE) use microprocessors that are very similar to or the same as those used in small servers and desktops. This work rethinks the design of microprocessors specifically for data center use along with how microprocessors are affected by the novel economic models that have been popularized by IaaS clouds. This talk will describe several architectural changes including how a processor can be decomposed into...

As transistor sizes continue to scale, we are about to witness stunning levels of chip integration, with 1,000 (simple) cores on a single die, and increasing levels of die stacking. Transistors may not be much faster, but there will be many more of them. In these architectures, energy and power will be the main constraint, efficient communication and synchronization a major challenge, and programmability an unknown. In this context, this talk presents some of the technologies that we will...

Since early 2000, we have been experiencing two very important developments in computing. One is that a tremendous amount of resources have been invested into innovative applications such as first-principle based models, deep learning and cognitive computing. Many application domains are questioning the conventional “it is too expensive” thinking that led to inaccuracies and missed opportunities. The other part is that the industry has been taking a technological path where application...