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BrainChip Showcases Foundation for Next Generation AI Solutions at AI Hardware & Edge AI Summit

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BrainChip Holdings Ltd participates in the AI Hardware & Edge AI Summit to showcase its Akida platform for Edge AI devices
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  • BrainChip's Akida platform provides efficient AI inference on device, reducing latency and improving privacy and data security
  • Akida processors power the next generation of Edge AI devices in various industries
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LAGUNA HILLS, Calif.--(BUSINESS WIRE)-- 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, is a Gold Sponsor participant of the AI Hardware & Edge AI Summit September 12-14 at the Santa Clara Marriott in Santa Clara, Calif.

The combined AI Hardware & Edge AI Summit comprehensively covers the design and deployment of ML hardware and software infrastructure across the cloud-edge continuum. As part of the Summit, BrainChip CMO Nandan Nayampally will present “The Edge of Tomorrow: Intelligent Compute to scale AIoT” September 13 at 4:55 p.m. PDT. The session will detail BrainChip’s holistic, distributed approach that frees up the cloud and creates an explosion of capable, intelligent sensing devices on the Edge that accelerate global artificial intelligence.

“We are pleased to partner with the AI Hardware & Edge AI Summit to demonstrate how BrainChip’s fully digital, event-based Akida™ platform provides radically efficient AI inference on device to substantially increase the level of intelligence delivered on Edge devices,” said Nayampally. “I look forward to discussing how our approach in keeping AI/ML local to the chip while minimizing the need for cloud, dramatically reduces latency while improving privacy and data security.”

Akida processors power the next generation of Edge AI devices that enable growth in intelligence in industrial, home, automotive, and other IoT environments. Akida’s fully digital, customizable, event-based neural processing solution is ideal for advanced intelligent sensing, medical monitoring and prediction, high-end video-object detection and more. Akida’s neuromorphic architecture delivers high performance with extreme energy efficiency enabling AI solutions previously not possible on battery-operated or fan-less embedded Edge devices. Akida also has a unique ability to securely learn on-device without the need for cloud retraining.

To schedule an appointment with BrainChip during the AI Hardware & Edge AI Summit, email sales@brainchip.com.

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)

BrainChip is the worldwide leader in Edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, Akida™, uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Akida uniquely enables Edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. Akida Neural processor IP, which can be integrated into SoCs on any process technology, has shown substantial benefits on today’s workloads and networks, and offers a platform for developers to create, tune and run their models using standard AI workflows like Tensorflow/Keras. In enabling effective Edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.

Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006

Media Contact:

Mark Smith

JPR Communications

818-398-1424

Investor Contact:

Tony Dawe

Director, Global Investor Relations

BrainChip

tdawe@brainchip.com

Source: BrainChip Holdings Ltd

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