Hardware Podcasts

BrainChip’s IP for Targeting AI Applications at the Edge

EE Times Current · 7 Feb 2025 · 47m

What this episode covers

Dr. Tony Lewis, CTO of BrainChip, and scientists Dr. Rudy Rupay, Dr. Olivier Quinan, and Dr. Chris Carlson discuss the company's temporal event-based neural network (TENN) and its application in the Akida chip. They explain how TENN uses state-space methods and Legendre polynomials to achieve stable training and compact, low-power inference at the edge, outperforming models like Mamba and Transformers in specific tasks. The team highlights BrainChip's IP licensing model, their recent success in eye-tracking and speech recognition competitions, and the upcoming Akida 2.0 architecture which supports multi-bit resolution for richer algorithmic flexibility.

Most relevant to

early-stage companies

Topics

Mentioned

Companies
BrainChip, NVIDIA
Products
Akida, Akida 2.0
Also
TENN, Mamba, CVPR 2024

Related episodes from other shows

Other shows on the same ground, closest first.

  • The Next Byte Podcast · 23m

    110. The Future of Neurology: Neural Interfaces Countering Neural Diseases

    Hosts Daniel and Forbode discuss NeuralTree, a neurochip developed by researchers at EPFL (École Polytechnique Fédérale de Lausanne) designed to detect and counter neural disorders like epilepsy and Parkinson's disease. The chip features 256 input channels, an area of 3.5 square millimeters, and an energy-aware machine learning algorithm that allows it to operate efficiently on the edge without cloud dependency. The hosts highlight the device's ability to distinguish between healthy and irregular neural impulses, its potential for self-evolving algorithms to adapt to changing brain signals, and its broader implications for neurological research and treatment.

    Embedded firmwareMicrocontrollers & dev platforms
    7 chapters

  • Embedded.fm · 1h 24m

    497: Everyone Likes Tiny

    Kwabena Agyeman, President and Co-Founder of OpenMV, discusses the launch of two new programmable camera systems, the OpenMV Cam N6 and the OpenMV AE3, via a Kickstarter campaign. He explains how integrating Neural Processing Units (NPUs) and Arm Helium SIMD instructions enables high-performance computer vision and edge AI on microcontrollers, achieving significant speedups while maintaining ultra-low power consumption. Agyeman details the software ecosystem, including MicroPython, TensorFlow Lite for Microcontrollers, and partnerships with Edge Impulse and Roboflow, which simplify model training and deployment for embedded developers.

    Embedded firmwareMicrocontrollers & dev platforms
    8 chapters

  • IoT For All Podcast · 19m

    The Evolution of AI in IoT | BrainChip's Nandan Nayampally | Internet of Things Podcast

    Nandan Nayampally, CMO at BrainChip, discusses the evolution of AIoT and the shift of AI processing from the cloud to the edge. He explains how neuromorphic computing enables real-time, private, and efficient inference on billions of devices, citing PwC's $15 trillion GDP impact estimate. Nayampally highlights key adoption markets like healthcare, agriculture, and infrastructure, and advises companies to focus on solving acute pain points ('headaches') rather than nice-to-have features ('vitamins') to gain traction.

    AI & machine learningConnected products & IoT
    5 chapters

  • Coredump Sessions · 58m

    #004: The Future of Edge AI and What it Means for Device Makers

    Alex Samuelson, CTO and co-founder of Imagimob, joins Memfault founders Francois Baldassari and Chris Coleman to discuss the practical implementation of Edge AI on embedded devices. They highlight how hardware accelerators like the Cortex-M55 and U55 have enabled GPU-like performance on microcontrollers, allowing for real-time inference in applications such as welding quality monitoring and fall detection. The conversation emphasizes that data collection, labeling, and memory constraints are the primary bottlenecks, requiring device makers to build data acquisition firmware early in the development process. Finally, they discuss the shift toward 'AI as a Service' business models, where hardware companies generate recurring revenue by updating models over the device's lifecycle.

    AI & machine learningMicrocontrollers & dev platforms
    8 chapters

  • Hackaday Podcast · 52m

    Ep009: On the Edge of AI, Comment Your Code, Big Big Wheels, and Makers of Munich

    Hackaday editors Mike Szczys and Elliot Williams discuss new AI-on-the-edge hardware from BeagleBone and Google, the reverse engineering of a keytar firmware, and a critical analysis of Tesla's self-driving safety data. They also cover a custom smart lamp build, flexible PCB contests, and Elliot provides an audio tour of the Make Munich maker fair.

    AI & machine learningDebugging & failure analysis
    8 chapters

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by EE Times Current. The episode itself belongs to its publisher.