Laguna Hills, Calif. – March 6, 2023 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power, fully digital, neuromorphic AI IP, today announced the second generation of its Akida platform that drives extremely efficient and intelligent edge devices for the Artificial Intelligence of Things (AIoT) solutions and services market that is expected to be $1T by 2030. This hyper-efficient yet powerful neural processing system, architected for embedded Edge AI applications, now adds efficient 8-bit processing to go with advanced capabilities such as time domain convolutions and vision transformer acceleration, for an unprecedented level of performance in sub-watt devices, taking them from perception towards cognition. The second-generation of Akida now includes Temporal Event Based Neural Nets (TENN) spatial-temporal convolutions that supercharge the processing of raw time-continuous streaming data, such as video analytics, target tracking, audio classification, analysis of MRI and CT scans for vital signs prediction, and time series analytics used in forecasting, and predictive maintenance. These capabilities are critically needed in industrial, automotive, digital health, smart home and smart city applications. The TENNs allow for radically simpler implementations by consuming raw data directly from sensors – drastically reduces model size and operations performed, while maintaining very high accuracy.

Laguna Hills, Calif. – February 26, 2023 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that it has entered into a partnership with emotion3D to demonstrate in-cabin analysis that makes driving safer and enables next level user experience. This analysis enables a comprehensive understanding of humans and objects inside a vehicle. The partnership will allow emotion3D to leverage BrainChip's technology to achieve an ultra-low-power working environment with on-chip learning while processing everything locally on device within the vehicle to ensure data privacy. "We are committed to setting the standard in driving safety and user experience through the development of camera-based, in-cabin understanding," says Florian Seitner, CEO at emotion3D. "In combining our in-cabin analysis software with BrainChip's on-chip compute, we are able to elevate that standard in a faster, safer and smarter way. This partnership will provide a cascading number of benefits that will continue to disrupt the mobility industry."

Laguna Hills, Calif. – November 22, 2022 –BrainChip Holdings Ltd(ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power neuromorphic AI IP, today announced that the Rochester Institute of Technology (RIT) has joined the University AI Accelerator Program to ensure students have the tools and resources needed to encourage development of cutting-edge technologies that will continue to usher in an era of essential AI solutions. Rochester Institute of Technology (RIT) is a highly accredited technology institute with AI engineering programs that conduct research on fundamental and applied topics in artificial intelligence. These include algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. BrainChip's University AI Accelerator Program provides hardware, training and guidance to students at higher education institutions with existing AI engineering programs. Students participating in the program will have access to real-world, event-based technologies offering unparalleled performance and efficiency to advance their learning through graduation and beyond.

Laguna Hills, Calif. – DATE, 2022 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power neuromorphic AI IP, has extended the breadth and depth of its neuromorphic IP with two new patents granted by the US Patents and Trademarks Office (USPTO), and the acquisition of previously licensed technology from Toulouse Tech Transfer (TTT). These latest additions of technical assets reinforce BrainChip's event-based processor differentiation for high performance, ultra-low power AI inference and on-chip learning. BrainChip also acquired full ownership of the IP rights related to JAST learning rule and algorithms from French technology transfer-based company TTT, including issued patent EP3324344 and pending patents US2019/0286944 and EP3324343. The invention related to the acquired IP rights include pattern detection algorithms that provide BrainChip with significant competitive advantages. The company held an exclusive license for the IP prior to their acquisition.

LAGUNA HILLS, CA / ACCESSWIRE / June 19, 2022 /BrainChip Holdings Ltd (ASX:BRN)(OTCQX:BRCHF)(ADR:BCHPY), the world's first commercial producer of neuromorphic AI IP, and Prophesee, the inventor of the world's most advanced neuromorphic vision systems, today announced a technology partnership that delivers next-generation platforms for OEMs looking to integrate event-based vision systems with high levels of AI performance coupled with ultra-low power technologies. Inspired by human vision, Prophesee's technology uses a patented sensor design and AI algorithms that mimic the eye and brain to reveal what was invisible until now using standard frame-based technology. BrainChip's first-to-market neuromorphic processor, Akida, mimics the human brain to analyze only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Keeping AI/ML local to the chip, independent of the cloud, also dramatically reduces latency. "We've successfully ported the data from Prophesee's neuromorphic-based camera sensor to process inference on Akida with impressive performance," said Anil Mankar, Co-Founder and CDO of BrainChip.

When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considerations, the use of larger batch sizes is necessary. While past work has shown that larger batch sizes decrease learning efficiency from a learning curve perspective, it remains an open question how batch size impacts methods for stopping active learning. We find that large batch sizes degrade the performance of a leading stopping method over and above the degradation that results from reduced learning efficiency. We analyze this degradation and find that it can be mitigated by changing the window size parameter of how many past iterations of learning are taken into account when making the stopping decision. We find that when using larger batch sizes, stopping methods are more effective when smaller window sizes are used.

This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications. Algorithms based on closest-to-hyperplane selection and query-by-committee selection are combined with methods for addressing imbalance such as positive amplification based on prevalence statistics from initial random samples. Three algorithms (ClosestPA, QBagPA, and QBoostPA) are presented and carefully evaluated on datasets for text classification and relation extraction. The ClosestPA algorithm is shown to consistently outperform the other two in a variety of ways and insights are provided as to why this is the case.