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Christopher Batten

Researcher at Cornell University

Publications -  64
Citations -  2404

Christopher Batten is an academic researcher from Cornell University. The author has contributed to research in topics: Computer science & Python (programming language). The author has an hindex of 20, co-authored 57 publications receiving 2177 citations. Previous affiliations of Christopher Batten include Massachusetts Institute of Technology.

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Proceedings ArticleDOI

Building Manycore Processor-to-DRAM Networks with Monolithic Silicon Photonics

TL;DR: A new monolithic silicon photonics technology suited for integration with standard bulk CMOS processes, which reduces costs and improves opto-electrical coupling compared to previous approaches is presented, which supports dense wavelength-division multiplexing with dozens of wavelengths per waveguide.
Proceedings ArticleDOI

Silicon-photonic clos networks for global on-chip communication

TL;DR: In this paper, the authors explore using photonics to implement low-diameter non-blocking crossbar and Clos networks, and show that a 64-tile photonic Clos network consumes significantly less optical power, thermal tuning power, and area compared to global photonic crossbars over a range of photonic device parameters.
Journal ArticleDOI

Building Many-Core Processor-to-DRAM Networks with Monolithic CMOS Silicon Photonics

TL;DR: A new monolithic silicon-photonic technology is introduced, which uses a standard bulk CMOS process to reduce costs and improve energy efficiency, and the logical and physical implications of leveraging this technology in processor-to-memory networks are explored.
Journal ArticleDOI

The Vector-Thread Architecture

TL;DR: SCALE, an instantiation of the vector-thread (VT) architecture designed for low-power and high-performance embedded systems, is presented and it is shown that its performance is competitive with larger and more complexprocessors.
Journal ArticleDOI

The vector-thread architecture

TL;DR: SCALE, an instantiation of the VT architecture designed for low-power and high-performance embedded systems, is presented and its performance is competitive with larger and more complex processors.