In my previous column, we discussed how the year 2030 seems set to be an exciting time to be in artificial intelligence (AI) and machine learning (ML) space (where no one can hear you scream). For example, in addition to the industrial IoT (IIoT) we also have the artificial intelligence of things (AIoT). Well, according to the IoT Agenda, "The AIoT is the combination of AI technologies with the IoT infrastructure to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics […] the AIoT is transformational and mutually beneficial for both types of technology as AI adds value to IoT through machine learning capabilities and IoT adds value to AI through connectivity, signaling, and data exchange." I've said it before and I'll say it again, I couldn't have said this any better myself. As we noted in Part 1 of this 2-part miniseries, the folks at PwC project that the impact of AI on global GDP by 2030 will be around $15T.

Artificial Intelligence (AI) has been making headlines over the past few months with the widespread use and speculation about generative AI, such as Chat GPT. However, AI is a broad topic covering many algorithmic approaches to mimic some of the capabilities of human beings. There is a lot of work going on to use various types of AI to assist humans in their various activities. Note that all AI has limitations. It generally doesn't reason, like we do and it is generally best at recognizing patterns and using that information to create conclusions or recommendations.

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.

With artificial intelligence and machine learning (AI/ML) processors and coprocessors roaring across the embedded edge product landscape, the quest continues for high-performance technology that can run a wide range of AI/ML models with very low power consumption. Brainchip has been marketing a line of unique, bio-inspired Akida line of licensable, configurable neural processing IP for a while now. The company's synthesizable IP is designed to efficiently implement AI/ML workloads as an on-chip CPU coprocessor that demands little CPU intervention. Now, the company has introduced its second-generation Akida AI/ML neural-processing coprocessor architecture, which improves upon the first-generation IP architecture in several ways. The company's Akida IP runs standard neural-network models built with standard AI/ML tool flows, but this IP uniquely leverages the energy-efficient organic brain architectures produced over more than 100 million years of evolution, implementing cognitive AI/ML processing using a bio-inspired but fully digital approach to neural processing.

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.

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.

We are initiating coverage of BrainChip Holdings (OTC:BRCHF) with a valuation of $0.75 per share. BrainChip is the first company to offer a commercial neuromorphic processor and the associated IP to the market. The company's Akida IP brings artificial intelligence (AI) tools to the "edge" with on-device computing and "one-shot" learning capabilities. The company licenses its intellectual property to OEMs, semiconductor designers and semiconductor manufacturers. On-device Artificial Intelligence or "Edge AI" holds significant promise as a low-power alternative to Cloud AI tools currently in the marketplace.

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.

Edge Impulse, the leading platform for enabling ML at the edge, and BrainChip, the leading provider of neuromorphic AI IP technology, announced support for deploying Edge Impulse projects on the BrainChip MetaTF platform. Edge Impulse enables developers to rapidly build enterprise-grade ML algorithms, trained on real sensor data, in a low to no code environment. These trained algorithms can now be quantized, optimized and converted to Spiking Neural Networks (SNN), which are compatible and can be deployed with BrainChip Akida devices. This capability is available for new and existing Edge Impulse projects by using the BrainChip MetaTF model deployment block integrated on the platform. This deployment block enables free-tier developers and enterprise developer users to create and validate neuromorphic models for real-world use-cases and deploy on BrainChip Akida development kits.