This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. We all know the AI revolution train is heading straight for the Embedded Station. Some of us are already in the driver’s seat, while others are waiting for the first movers to pave the way so we can […]
Right Sizing AI for Embedded Applications Read More +
Laguna Hills, Calif. — November 4th, 2025 — BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), a global leader in ultra-low power, fully digital, event-based neuromorphic AI, today announced the launch of its AKD1500, a neuromorphic Edge AI accelerator co-processor chip, at Embedded World North America. Designed to deliver exceptional performance with minimal power consumption, the AKD1500 achieves 800 giga operations
This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. Accelerating LiDAR Point Cloud with BrainChip’s Akida™ PointNet++ Model. LiDAR (Light Detection and Ranging) technology is the key enabler for advanced Spatial AI—the ability of a machine to understand and interact with the physical world in three
Unleash Real-time LiDAR Intelligence with BrainChip Akida On-chip AI Read More +
This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. In the rapidly evolving field of artificial intelligence, edge computing has become increasingly vital for deploying intelligent systems in real-world environments where power, latency, and bandwidth are limited: we need neural network models to run efficiently. For
Akida Exploits Sparsity For Low Power in Neural Networks Read More +
Aligns with BrainChip’s long-term strategy to accelerate customer access to its innovations and reduce development cycles 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, today announced launch of the BrainChip Developer Akida Cloud, a new cloud-based access point
This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. ChatGPT was released to the public on November 30th, 2022, and the world – at least, the connected world – has not been the same since. Surprisingly, almost three years later, despite massive adoption, we do not
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Stephan Sokolov, Software Engineer at DeGirum, demonstrates the company’s latest edge AI and vision technologies and products in BrainChip’s booth at the 2025 Embedded Vision Summit. Specifically, Sokolov demonstrates the power of real-time AI inference at the edge, running DeGirum’s PySDK application directly on BrainChip hardware. This demo showcases low-latency, high-efficiency performance as a script
DeGirum Demonstration of Its PySDK Running on BrainChip Hardware for Real-time Edge AI Read More +
Kurt Manninen, Senior Solutions Architect at BrainChip, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Van Manninen demonstrates his company’s large language models (LLMs) running on an FPGA edge device, powered by BrainChip’s proprietary TENNs (Temporal Event-Based Neural Networks) framework. BrainChip enables real-time generative AI
Richard Resseguie, Senior Product Manager at BrainChip, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Van Resseguie demonstrates the company’s latest advancements in edge audio AI. The demo features a suite of models purpose-built for real-world applications including automatic speech recognition, denoising, keyword spotting, and
BrainChip Demonstration of Its Latest Audio AI Models in Action At the Edge Read More +
Tony Lewis, Chief Technology Officer at BrainChip, presents the “State-space Models vs. Transformers for Ultra-low-power Edge AI” tutorial at the May 2025 Embedded Vision Summit. At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space
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