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职称:professor
所属学校:Stanford University
所属院系:Computer Science
所属专业:Computer Science
联系方式:(650) 725-8945
Bill Dally is the Willard R. and Inez Kerr Bell Professor of Computer Science and Electrical Engineering and former Chairman of the Computer Science Department at Stanford University. He is a member of the Computer Systems Laboratory, leads the Concurrent VLSI Architecture Group, and teaches courses on Computer Architecture, Computer Design, and VLSI Design. He is a Member of the National Academy of Engineering , a Fellow of the American Academy of Arts & Sciences , a Fellow of the IEEE, a Fellow of the ACM, received the ACM Maurice Wilkes Award in 2000, the IEEE Seymour Cray Award in 2004, and the ACM Eckert Mauchly Award in 2010. He has an h-index of 60. Before coming to Stanford, Bill was a Professor in the department of Electrical Engineering and Computer Science at MIT .
ELM: The Efficient Low-Power Microprocessor We are developing a programmable architecture that is easily programmable in a high-level language ("C") and at the same time has performance per unit power competitive with hard-wired logic, and 20-30x better than conventional embedded RISC processors. This power savings is achieved by using more efficient mechanisms for instruction supply, based on compiler managed instruction registers, and data supply, using a deeper register hierarchy and indexable registers. Enabling Technology for On-Chip Networks As CMPs and SoCs scale to include large numbers of cores and other modules, the on-chip network or NoC that connects them becomes a critical systems component. We are developing enabling technology for on-chip networks including network topologies, flow control mechanisms, and router organizations. For example, our flattened butterfly topology offers both lower latency and substantially reduced power compared to conventional on-chip mesh or ring networks. Sequoia: Programming the Memory Hierarchy Sequoia is a programming language that is designed to facilitate the development of memory hierarchy aware parallel programs that remain portable across modern machines with different memory hierarchy configurations. Sequoia abstractly exposes hierarchical memory in the programming model and provides language mechanisms to describe communication vertically through the machine and to localize computation to particular memory locations within the machine. A complete Sequoia programming system has been implemented, including a compiler and runtime systems for both Cell processors and distributed memory clusters, that delivers efficient performance running Sequoia programs on both of these platforms. An alpha version of this programming system will soon be made public. Scalable Network Fabrics We are developing architectures and technologies to enable large, scalable high-performance interconnection networks to be used in parallel computers, network switches and routers, and high-performance I/O systems. Recent results include the development of a hierarchical network topology that makes efficient use of a combination of electrical and optical links, a locality-preserving randomized oblivious routing algorithm, a method for scheduling constrained crossbar switches, new speculative and reservation-based flow control methods, and a method for computing the worst-case traffic pattern for any oblivious routing function.