Co-design of a novel CMOS highly parallel, low-power, multi-chip neural network accelerator
W Hokenmaier, R Jurasek, E Bowen, R Granger, D Odom

TL;DR
This paper introduces the NV-1, a novel low-power ASIC AI processor with highly parallel architecture that significantly accelerates processing and reduces energy consumption, enabling advanced edge devices.
Contribution
The paper presents the design, development, and validation of the NV-1, a highly parallel, low-power neural network accelerator with innovative architecture and communication protocols.
Findings
>10X processing speedup
>100X energy reduction
Successful real-world deployment
Abstract
Why do security cameras, sensors, and siri use cloud servers instead of on-board computation? The lack of very-low-power, high-performance chips greatly limits the ability to field untethered edge devices. We present the NV-1, a new low-power ASIC AI processor that greatly accelerates parallel processing (> 10X) with dramatic reduction in energy consumption (> 100X), via many parallel combined processor-memory units, i.e., a drastically non-von-Neumann architecture, allowing very large numbers of independent processing streams without bottlenecks due to typical monolithic memory. The current initial prototype fab arises from a successful co-development effort between algorithm- and software-driven architectural design and VLSI design realities. An innovative communication protocol minimizes power usage, and data transport costs among nodes were vastly reduced by eliminating the address…
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