BrainChip
Ms. Le has a remarkable professional history, both technologically and in executive management, having retired from Texas Instruments (TI) as a Senior Fellow after 35 years. While at TI, she led the global R&D, manufacturing operation and high-volume production of TI’s multi-billion-dollar memory, DSP, and base station product lines. Ms. Le holds 24 patents and serves on the board of two universities. In addition to BrainChip, she currently serves on the boards of Wolfspeed, National Instruments, Ballard Power Systems and Atomera. She was inducted into Women in Technology Hall of Fame and became the first engineer to be inducted into Asian Hall of Fame. She received numerous recognitions for her philanthropic contributions worldwide, including Congressional Special Recognition.
Ms. Le serves on the BrainChip board as a non-executive director and is a member of the Audit & Risk and Remuneration & Nominations Committees.
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BrainChip
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BrainChip is a global technology company that has developed a revolutionary advanced neural networking processor that brings artificial intelligence to the edge in a way that existing technologies are not capable. The solution is high performance, small, ultra-low power and enables a wide array of edge capabilities that include local training, learning and inference. The company markets an innovative event-based neural network processor that is inspired by the spiking nature of the human brain and implements the network processor in an industry standard digital process. By mimicking brain processing, BrainChip has pioneered a spiking neural network, called Akida™, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than transmission to the cloud or a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint data centers.