As AI services and applications grow exponentially, including the latest burgeoning GenAI trend, the industry is rapidly transitioning to a Hybrid AI model that splits the compute needed between the Cloud and an Edge device. The need for specialized AI processing on edge devices is becoming almost mandatory. This is driven by a multitude of factors including speech and visual processing for improved Human Machine Interfaces, time-series, or signal processing for applications like vital signs prediction in healthcare and preventative maintenance in factory automation, just to name a few, along with the general need for privacy and security. To meet portability, responsiveness, and cost requirements, future proofed AIoT devices will need to be equipped with efficient compute for advanced sequence prediction, semantic segmentation, multimedia processing, and multi-dimensional time series processing in a very low power budget and silicon footprint.
Nandan Nayampally
Nandan is an entrepreneurial executive with over 25 years of success in building or growing disruptive businesses with industry-wide impact. Nandan was most recently at Amazon leading the delivery of Alexa AI tools for Echo, Fire TV and other consumer devices. Prior to that he spent more than 15 years at Arm Inc. including roles as GM developing Arm’s CPU and broader IP portfolio into an industry leader that is built into over 100B chips. He started his career at AMD on their very successful Athlon processor program. He also helped grow product lines in startup businesses such as Silicon Metrics and Denali Software which had successful acquisitions down the road.
BrainChip
Website: https://brainchip.com/
BrainChip is a leader in edge AI on-chip processing and learning. The company’s first-to-market convolutional, neuromorphic processor, AkidaTM, mimics the event-based processing method of the human brain in digital technology to classify sensor data at the point of acquisition, processing data with unparalleled energy-efficiency and independent of the CPU or MCU with high precision. On-device learning that is local to the chip without the need to access the cloud dramatically reduces latency while improving privacy and data security. In enabling effective edge computing to be universally deployable across real-world applications, such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI is the future for customers’ products, the planet and beyond.
