Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research.

The company's event-based digital Neuromorphic IP can add efficient AI processing to SoCs. Edge AI is becoming a thing. Instead of using just an embedded microprocessor in edge applications and sending the data to a cloud for AI processing, many edge companies are considering adding AI at the edge itself, and then communicating conclusions about what the edge processor is "seeing" instead of sending the raw sensory data such as an image. To date, this dynamic has been held back by the cost and power requirements of initial implementations. What customers are looking for is proven AI tech that can run under a watt, and that they can add to a microcontroller for on-board processing.

Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops.

BrainChip Holdings Ltd., a leading provider of ultra-low power, high-performance AI technology, ended the 2020 calendar year having made significant strides in the development of its technology backed by the launch of its Early Access Program (EAP), availability of Akida evaluation boards, new partnerships, and expansion of its executive leadership and global facilities. "This past year saw significant progress in the development of the Akida technology in terms of both market readiness and the increase in market possibilities that the solution will provide immediate impact in" The Company's EAP was launched in June targeting specific customers in a diverse set of end markets in order to ensure availability of initial devices and evaluation systems for key applications. Multiple customers have committed to the advanced purchase of evaluation systems for a range of strategic Edge applications including Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV), Unmanned Aerial Vehicles (UAV), Edge vision systems and factory automation. Among those joining the EAP include VORAGO Technologies in a collaboration intended to support a Phase I NASA program for a neuromorphic processor that meets spaceflight requirements. BrainChip is also collaborating with Tier-1 Automotive Supplier Valeo Corporation to develop neural network processing solutions for ADAS and AV.

San Francisco, March 3, 2021 -- BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low-power, high-performance AI technology, ended the 2020 calendar year having made significant strides in the development of its technology backed by the launch of its Early Access Program (EAP), availability of Akida evaluation boards, new partnerships, and expansion of its executive leadership and global facilities. The Company's EAP was launched in June targeting specific customers in a diverse set of end markets in order to ensure availability of initial devices and evaluation systems for key applications. Multiple customers have committed to the advanced purchase of evaluation systems for a range of strategic Edge applications including Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV), Unmanned Aerial Vehicles (UAV), Edge vision systems and factory automation. Among those joining the EAP include VORAGO Technologies in a collaboration intended to support a Phase I NASA program for a neuromorphic processor that meets spaceflight requirements. BrainChip is also collaborating with Tier-1 Automotive Supplier Valeo Corporation to develop neural network processing solutions for ADAS and AV.