Founded Year

2016

Stage

Series B | Alive

Total Raised

$29M

Last Raised

$14M | 2 yrs ago

About GrAI Matter Labs

GrAI Matter Labs is focused on ultra-low power neuromorphic computing for sensor analytics and machine learning – powered by brain-inspired technology. The company's unique neuromorphic computing paradigm overcomes the limitations of Von Neumann machines, offering massively parallel and fully programmable sensor analytics and machine learning at significantly reduced power consumption.

GrAI Matter Labs Headquarter Location

2880 Zanker Road Suite 203

San Jose, California, 95134,

United States

GrAI Matter Labs's Product Videos

GrAIMatterLabs GrAIVIP Demo.png

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GrAI Matter Labs's Products & Differentiation

See GrAI Matter Labs's products and how their products differentiate from alternatives and competitors

  • Life-Ready GrAI VIP

    World’s first Dataflow and sparsity-native System-On-Chip (SOC) delivering Resnet-50 inferences at ~1ms.

    Differentiation

    bit FP precision at low power, size and real-time AI performance 

Expert Collections containing GrAI Matter Labs

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

GrAI Matter Labs is included in 3 Expert Collections, including Artificial Intelligence.

A

Artificial Intelligence

9,071 items

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.

C

Conference Exhibitors

5,302 items

S

Semiconductors, Chips, and Advanced Electronics

6,045 items

Companies in this collection develop everything from microprocessors to flash memory, integrated circuits specifically for quantum computing and artificial intelligence to OLED for displays, massive production fabs to circuit design firms, and everything in between.

GrAI Matter Labs Patents

GrAI Matter Labs has filed 2 patents.

The 3 most popular patent topics include:

  • Electrophysiology
  • Ion channels
  • Acid fast bacilli
patents chart

Application Date

Grant Date

Title

Related Topics

Status

3/26/2020

Artificial neural networks, Image processing, Electrophysiology, Ion channels, Computational neuroscience

Application

Application Date

3/26/2020

Grant Date

Title

Related Topics

Artificial neural networks, Image processing, Electrophysiology, Ion channels, Computational neuroscience

Status

Application

Latest GrAI Matter Labs News

GrAI Matter Labs: Brain-Inspired AI For The Edge

May 27, 2022

Founder and Principal Analyst,Cambrian-AI Research LLC May 27, 2022, This article was written by Alberto Romero, Cambrian-AI Analyst, and Karl Freund, Cambrian-AI Founder. We recently tweeted about the startup GrAI Matter Labs (GML) and received a lot of questions about the company’s products and strategy. As one of the first startups to launch a neuromorphic AI platform for edge AI, the company is deserving of a little more attention, so let’s take a closer look. Background GML is an AI hardware start-up targeting Edge AI with near-real-time computation. Founded in 2016 by a group of experts in silicon design and neuromorphic computing, GML believes they are revolutionizing inference at the endpoint device, focussing initially on audio and video processing with very low latencies. By processing data closest to its source, AI algorithms can provide almost instant insight and transformation without incurring the higher latencies and costs typical of cloud servers. GML’s “Life-ready” AI provides solutions that here-to-for were simply impossible at such low cost and power. After a demo, we were amazed by the quality and instant latencies they were able to produce. GML is currently focused on industrial robotics and drones for near-sensor understanding, which is a ~50M device market in 2025. Now the company’s plans will expanding its reach to include high-fidelity data transformation in mobile and consumer devices, a market which the company estimates is 20 times larger with over 1 billion devices in 2025. GML is turning its attention to data transformation in consumer devices. GML High-fidelity Transforming content at the endpoint device with high fidelity IoT devices are proliferating in smart security cameras in the streets, robotic arms in factories, voice assistants in our homes, and smartphones in our pockets. All these devices have sensors that capture data. Most companies applying AI at the edge of the network are focusing on understanding or categorizing that data to enable predictions. GML is literally transforming the audio-visual user experience on the fly. To achieve this, they combine four pillars of technology: high-precision (16-bit Floating-Point) processing to deliver high-quality content, dynamic data flow to exploit data-dependent sparsity, neuromorphic design to improve efficiency, and in-memory computing to reduce power consumption and latency. The bottom line: 1/10th the response time at 1/10th the power. MORE FOR YOU GML’s value proposition is therefore building on these pillars that, combined, create a uniquely differentiated solution: Endpoint computing with AI at low latency and high-power efficiency to transform raw data into high-fidelity consumable content in real-time, allowing for instant applicability in many daily situations. Sparsity is the key to transforming content at low latency and low power Power restrictions at the edge of the network force endpoint AI devices to keep consumption low. GML’s innovative solution produces high fidelity content by exploiting sparsity — the fact that audio and video content doesn’t change everywhere, nor all at once — at high precision. A prototypical example to illustrate the upside of this approach is a smart security camera. The recorded background remains largely constant across the day, so it gives no new information. By processing and analyzing only people, vehicles, and other moving objects, the savings in power consumption and reductions in latency can range up to 95%. A silicon implementation of GML’s solution: GrAI VIP GML’s forthcoming hardware concept, GML VIP (not yet available for production) is an SoC (System on Chip) that integrates a neuron engine, GrAICore, with the required characteristics for low-power, ultra-low latency, and high-precision inference processing at the endpoint. GrAICore employs brain-inspired NeuronFlow technology. Apart from sparse processing, NeuronFlow is based on the dataflow architecture paradigm, which allows for efficient fine-grained parallelization. Together with in-memory compute, which reduces performance bottlenecks caused by moving data between memory and processor, these features accelerate the computations by several orders of magnitude. VIP’s full-stack is completed with the GrAIFlow SDK, compatible with the usual ML frameworks, TensorFlow and PyTorch, to implement custom models. It also provides a library of ready-to-deploy models. Both custom and pre-trained models can be optimized and compiled with the ML toolkit to be deployed for inference at the edge device with the last component, the GrAIFlow Run-Time Ready. Conclusions GML is targeting the $1 billion+ fast-growing market (20%+ per year) of endpoint AI with a unique approach backed by innovative technology. They best endpoint competitors by focusing on high-fidelity 16-bit floating-point real-time “content transformation” instead of just “understanding” (categorizing) which typically uses 8-bit computation. GML has better performance on Resnet50 compared to other edge devices from Intel, Google, and ... [+] NVIDIA. GrAI Matter Labs According to the company, the four pillars combine to outperform NVIDIA’s leading-edge platform, the Jetson Nano, by 10X, at > 10X lower power for Resnet50. However, we note that the Jetson Nano is a comprehensive edge platform, while the GML platform is focussed on doing a few tasks very well. GML potentially stands to revolutionize consumer and enterprise audio-visual experiences with everyday devices at high fidelity while meeting the strict power and cost requirements of endpoint content manipulation. We believe GML’s unique differentiation could help the company grow rapidly in a segment where they can enjoy a first-mover advantage. Follow me on  Twitter  or  LinkedIn . Check out my  website . Disclosures: This article expresses the opinions of the authors, and is not to be taken as advice to purchase from nor invest in the companies mentioned. Cambrian AI Research is fortunate to have many, if not most, semiconductor firms as our clients, including Blaize, Cerebras, D-Matrix, Esperanto, Graphcore, GML, IBM, Intel, Mythic, NVIDIA, Qualcomm Technologies, Si-Five, Synopsys, and Tenstorrent. We have no investment positions in any of the companies mentioned in this article and do not plan to initiate any in the near future. For more information, please visit our website at https://cambrian-AI.com .

GrAI Matter Labs Web Traffic

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Page Views per User (PVPU)
Page Views per Million (PVPM)
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  • When was GrAI Matter Labs founded?

    GrAI Matter Labs was founded in 2016.

  • Where is GrAI Matter Labs's headquarters?

    GrAI Matter Labs's headquarters is located at 2880 Zanker Road, San Jose.

  • What is GrAI Matter Labs's latest funding round?

    GrAI Matter Labs's latest funding round is Series B.

  • How much did GrAI Matter Labs raise?

    GrAI Matter Labs raised a total of $29M.

  • Who are the investors of GrAI Matter Labs?

    Investors of GrAI Matter Labs include 360 Capital Partners, iBionext, Bpifrance, Celeste Management and 3T Finance.

  • Who are GrAI Matter Labs's competitors?

    Competitors of GrAI Matter Labs include NVIDIA and 2 more.

  • What products does GrAI Matter Labs offer?

    GrAI Matter Labs's products include Life-Ready GrAI VIP.

  • Who are GrAI Matter Labs's customers?

    Customers of GrAI Matter Labs include ADLINK, FRAMOS, ERM, EZ-Wheel and DMP.

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