BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Yoda
Regular
BrainShit
Regular
Turns out satsearch also offers ANT61’s Beacon, currently in two sizes: S and M.
Beacon S -- Satellite Communications & Real-Time Diagnostics Solution | satsearch
The ultimate satellite recovery and real time diagnostics solution.satsearch.co
View attachment 84907
View attachment 84910
Beacon M – Advanced Satellite Recovery & Telemetry Module | satsearch
Real-time telemetry and satellite recovery module for LEO missions. The Beacon M provides encrypted two-way communication, firmware updates, and critical health monitoring, ensuring uninterrupted mission control connectivity even after failures.satsearch.co
View attachment 84920
ANT61’s own website now has further info about the various Beacon models (https://www.ant61.com/beacon)…
View attachment 84911
View attachment 84912
… and so does a YouTube video uploaded on 1 August 2024:
View attachment 84913 View attachment 84914
However, nowhere does it state that the ANT61 Beacon also has Akida inside, like the ANT61 Brain.
There have been conflicting messages from ANT61 about this, such as the post @BrainShit shared with us in January (I’m not sure about the source?):
View attachment 84917
View attachment 84916
… or this little exchange on LinkedIn started by @yogi commenting: “Congratulations and still Neuromorphic inside?”
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-447276
View attachment 84919
Could it possibly fall under “customisation available on request” as an optional neuromorphic add-on, like in the case of the EDGX DPU?
I'm quite sure that Mikhail A. and his team are working to implement Akida in the Beacon (as an option) and the Brain ;-)
Last edited:
smoothsailing18
Regular
Question please what date roughly do we expect the next quarterly report ?.
Jacob Kiraz
Member
What with the Lies
White Horse
Regular
Hi Bravo,
DEEPX has cropped up before. If you go to their website, you will have your memory refreshed.
DX-M1 delivers ultra-efficient AI performance, design achieves 25 TOPS (200 eTOPS) under 5W, significantly outperforming competitors in performance-to-power ratio.
Type: AI Accelerator AI Performance: 25 TOPS Power Consumption: 3~5 W
Last edited:
What AGM? Pretty sure there was no AGM according to Antonio.
FJ-215
Regular
From the BRN news release:
" We chose to partner with BrainChip because we believe that neuromorphic technology will bring the same exponential improvement in AI as 20 years ago. The shift from CPU to GPU opened doors to deep neural network applications, which are at the core of all AI technologies today. Neuromorphic technology is also perfect for space: the lower power consumption means less heat dissipation, and we can get up to five times more computational power for the same electrical power budget."
My bold.
brainchip.com
" We chose to partner with BrainChip because we believe that neuromorphic technology will bring the same exponential improvement in AI as 20 years ago. The shift from CPU to GPU opened doors to deep neural network applications, which are at the core of all AI technologies today. Neuromorphic technology is also perfect for space: the lower power consumption means less heat dissipation, and we can get up to five times more computational power for the same electrical power budget."
My bold.
Revolutionizing Space Infrastructure: ANT61 Smart Robots
BrainChip collaborates with ANT61 to integrate smart robotics into space infrastructure, enhancing maintenance and operational efficiency.
brainchip.com
Frangipani
Top 20
The revised paperon Uni Tübingen research involving Akida (
), which was partially funded by Sony AI, got accepted for ICRA 2025, the International Conference on Robotics & Automation in Atlanta (19-23 May).
Detection of Fast-Moving Objects with Neuromorphic Hardware | Andreas Ziegler
The results of #ICRA2025 are out, and I'm happy to share that our work "Detection of Fast-Moving Objects with Neuromorphic Hardware" has been accepted. Project page: https://lnkd.in/ew-U5-BD Preprint: https://lnkd.in/enJWM2Vt (Data and code will follow) This paper's journey started with Karl...www.linkedin.com
View attachment 76918
As mentioned previously, the additional co-author of the revised version, Sebastian Otte, who used to be a PhD student and postdoc at Uni Tübingen (2013-2023), became a professor at Uni Lübeck’s Institute for Robotics and Cognitive Systems in September 2023, where he heads the Adaptive AI research group. He also leads the University of Lübeck research team that is collaborating with Mercedes-Benz, Intel and other partners on the NAOMI4Radar project (using Loihi 2). Otte is a uni researcher who has got first-hand experience with both Akida and Loihi, and has also expressed his appreciation for both:
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-438969
The other co-author named in Andreas Ziegler’s LinkedIn post, whose Master’s thesis was the initial basis for the research at Uni Tübingen that was to follow and ultimately led to the paper now accepted for ICRA 2025, is Karl Vetter, who left academia last July to work for BrainChip’s partner Neurobus (who are also partnered with Prophesee and Intel).
After winning first place at the European Defense Tech Hackathon in Paris two months ago…
… the team from Neurobus must be keen on defending their title at the upcoming European Defense Tech Hackathon in Munich, held in conjunction with the renowned Munich Security Conference:
European Defense Tech Hub
We foster defense innovation in Europe by connecting builders, investors, and policymakerseurodefense.tech
View attachment 76920
View attachment 76921
Hopefully with a little help from Akida?
Hopefully, Andreas Ziegler from Uni Tübingen will have an appreciative audience game for some event-(camera)-ful table tennis talk
at the International Conference on Robotics and Automation (ICRA 2025) in Atlanta later today.He will be presenting his team’s research (which also involved Akida and was partially funded by Sony AI, where he was a Research Scientist Intern at their office in Switzerland from November 2023 to March 2024 as part of his ongoing PhD studies at Uni Tübingen) that led to the publication of the
paper “Detection of Fast-Moving Objects with Neuromorphic Hardware” (cf. https://lnkd.in/ew-U5-BD, where you’ll also find a video and a GitHub link): Detection of Fast-Moving Objects with Neuromorphic Hardware | Andreas Ziegler
#ICRA2025 is coming closer! I’ll be presenting our work, “Detection of Fast-Moving Objects with Neuromorphic Hardware” — more details here: https://lnkd.in/ew-U5-BD I was also accepted into the Doctoral Consortium (DC), where I’ll present my research on event-based vision for fast robot...
It is worthwhile to refresh our memories regarding the whereabouts of his co-authors Sebastian Otte and Karl Vetter, who are both Uni Tübingen alumni from the same Cognitive Systems research group, where Andreas Ziegler is currently pursuing his PhD (https://uni-tuebingen.de/fakultaete...che/informatik/lehrstuehle/kognitive-systeme/):
The other co-author named in Andreas Ziegler’s LinkedIn post, whose Master’s thesis was the initial basis for the research at Uni Tübingen that was to follow and ultimately led to the paper now accepted for ICRA 2025, is Karl Vetter, who left academia last July to work for BrainChip’s partner Neurobus (who are also partnered with Prophesee and Intel).
After winning first place at the European Defense Tech Hackathon in Paris two months ago…
… the team from Neurobus must be keen on defending their title at the upcoming European Defense Tech Hackathon in Munich, held in conjunction with the renowned Munich Security Conference:
European Defense Tech Hub
We foster defense innovation in Europe by connecting builders, investors, and policymakers
Frangipani
Top 20
Dein Wort in Gottes Ohr! ;-)
💥 NVIDIA vs. Intel, a tale of two tech giants. NVIDIA has surged past Intel in market cap. Surprising? Maybe not. Intel once dominated with its Pentium CPUs. But NVIDIA made a long-term bet, on… | Harry Tottman | 59 comments
💥 NVIDIA vs. Intel, a tale of two tech giants. NVIDIA has surged past Intel in market cap. Surprising? Maybe not. Intel once dominated with its Pentium CPUs. But NVIDIA made a long-term bet, on AI. And they stuck with it. It took years, not quarters. But now, as AI reshapes everything...
Found this interesting, Nvidia v Intel and Nvidia's plan to stick with AI, perhaps our plans to stick with the next shift with Neuromorphic will pay off too.
Frangipani
Top 20
Innatera just unveiled Pulsar – the world’s first mass-market neuromorphic microcontroller for real-time intelligence at the sensor edge.
And yes, it is commercially available now…
Above images are from the Press Kit: https://www.dropbox.com/scl/fo/xrpycuii9repikowerctl/AGZqj0VP0NLSuxST9CNY3Ok?
www.prnewswire.com
May 21, 2025, 08:59 ET
DELFT, Netherlands, May 21, 2025 /PRNewswire/ --
Innatera, a leading developer of neuromorphic processors, today announced the launch of Pulsar, its first commercially available microcontroller to bring brain-like intelligence into edge devices. Born from more than a decade of pioneering research, Pulsar delivers up to 100X lower latency and 500X lower energy consumption than conventional AI processors. With this breakthrough, Innatera brings a new class of ultra-efficient, brain-inspired intelligence directly to the sensor edge.
With sensors embedded in everything from wearables and smart homes to cars and industrial systems, the need for real-time, secure, energy-efficient data processing at the edge has never been greater. Pulsar tackles this challenge head-on by processing data locally and intelligently, at the sensor level – eliminating the need to rely on brute-force compute in power-hungry edge processors or data centers to make sense of sensor data.
"Pulsar is not just another AI chip – it represents a fundamental shift in how we bring intelligence to the edge," says Sumeet Kumar, co-founder and CEO of Innatera. "This launch is the culmination of over a decade of deep research and engineering in neuromorphic computing, combined with a groundbreaking heterogeneous architecture. It marks the moment that our brain-inspired technology becomes ready for mass-market deployment. As demand for real-time, power-efficient intelligence in edge devices continues to grow, Pulsar delivers the capabilities that traditional AI hardware simply can't – ultra-low latency, minimal power draw, and on-device decision-making. More importantly, it lays the foundation for a new class of intelligent systems that are adaptive, autonomous, and scalable. Pulsar is our first major step toward making that future a reality."
Built for what's next: A platform for scalable, real-world edge intelligence
Pulsar introduces a compute architecture based on Spiking Neural Networks (SNNs), a generational leap in AI hardware that processes data the way the brain does, focusing only on changes in input. This event-driven model dramatically reduces energy use and latency while delivering precise, real-time decision-making. Pulsar goes even further by combining neuromorphic compute with traditional signal processing in a revolutionary architecture. Integrating a high-performance RISC-V CPU and dedicated accelerators for Convolutional Neural Networks (CNNs) and Fast Fourier Transform (FFT), this architecture provides exceptional versatility on a single chip.
"Innatera's Pulsar chip has the potential to redefine what's possible at the edge," says David Harold, senior analyst, Jon Peddie Research. "By using brain-inspired Spiking Neural Networks, it brings real-time processing to ultra-low-power devices without leaning on the cloud. That means sensors that can think for themselves – faster responses, lower energy use, and smarter performance across everything from wearables to industrial systems."
Smarter products, longer battery life
Pulsar gives product teams a shortcut to smarter features that were previously off-limits due to size, power, or complexity. Filtering and interpreting sensor data locally keeps the main application processor asleep until truly needed, in some cases, eliminating the need for a main application processor or cloud computing, extending battery life by orders of magnitude. With sub-milliwatt power consumption, Pulsar makes always-on intelligence truly viable, enabling everything from sub-millisecond gesture recognition in wearables to energy-efficient object detection in smart home systems. For example, it achieves real-time responsiveness with power budgets as low as 600 µW for radar-based presence detection and 400 µW for audio scene classification.
"The combination of Innatera's Spiking Neural Processor (SNP) and Socionext's highly integrated, sophisticated radar sensor technology introduces a powerful new approach to reducing power consumption and minimizing false detections in challenging applications, such as battery-powered devices," says Matthias Neumann, Senior Marketing Manager Smart Sensor & Smart Devices at Socionext. "We are confident that this collaboration will accelerate the adoption of radar sensing solutions in the market, bringing cutting-edge technology to a wider range of industries."
Simpler integration for sensor makers
Pulsar transforms traditional sensors into self-contained intelligent systems. With its small memory footprint and efficient neural models, it fits into tight form factors while eliminating the need for heavy external compute and reducing reliance on complex, custom DSP pipelines. Sensor manufacturers can now deliver plug-and-play smart sensor modules, accelerating development and time to market.
"Aria Sensing is committed to providing advanced, highly intuitive Ultra-Wideband system-on-chip and complete solutions. The Pulsar microcontroller by Innatera facilitates real-time sensing with exceptional energy efficiency, thereby creating opportunities for continuous operation applications," says Alessio Cacciatori, Founder and CEO of Aria Sensing. "We are particularly enthusiastic about the potential of Pulsar's neuromorphic architecture to integrate brain-inspired intelligence into our state-of-the-art 1D/2D/3D high-resolution sensing systems, leading to enhanced speed, responsiveness, and significantly improved power efficiency."
Empowering developers with familiar tools and a new community
Innatera's Talamo SDK makes neuromorphic development approachable. Developers can build spiking models from scratch, in a PyTorch-based environment, simulate, optimize, and deploy with ease.
To further support this ecosystem, Innatera is launching its developer program, now open to early adopters. More than just a portal, it's the foundation of a growing community designed to accelerate innovation, share knowledge, and empower members to build the next generation of intelligent edge applications together. An upcoming open-source PyTorch frontend and marketplace will create an even more collaborative ecosystem for neuromorphic AI.
"Innatera's Pulsar marks a leap forward in edge intelligence. As a partner, we see their neuromorphic approach redefining what's possible in ultra-low-power, always-on AI – exactly the kind of innovation the edge AI ecosystem needs to thrive," says Pete Bernard, CEO, EDGE AI FOUNDATION.
"As a trusted solution partner for innovative semiconductor technologies, SmartSoC is proud to collaborate with Innatera to bring cutting-edge AI solutions like Pulsar to market. Pulsar's unique neuromorphic architecture perfectly complements our mission to deliver smarter, more efficient products to customers across Europe and India, enabling a new generation of intelligent edge applications," says Bharath Desareddy, CEO, SmartSoC Solutions.
Designed to unlock the future
Beyond what Pulsar delivers today, it lays the groundwork for what's next: edge AI systems that are autonomous, adaptive, and capable of learning in the field. With each product generation, Innatera's roadmap leads toward self-calibrating, self-optimizing devices that reduce maintenance costs and unlock entirely new classes of edge applications.
Pulsar is available now. Whether you're building the next breakthrough in wearables, enabling smarter industrial systems, or designing cutting-edge sensors, Pulsar is your gateway to the future of edge intelligence.
Learn more and get started at www.innatera.com/pulsar.
For the press kit, click here.
About Innatera
Innatera is a pioneering semiconductor company at the forefront of neuromorphic computing, a revolutionary approach to AI that emulates the brain's neural networks. Its ultra-low-power processors are designed to deliver real-time, high-performance AI inference for energy-constrained applications. Founded in 2018 and headquartered in Delft, Netherlands, Innatera is backed by leading investors and collaborates with global industry leaders to redefine the future of edge AI. The company's mission is to bring cognitive capabilities to devices, enabling smarter, faster, and more efficient decision-making directly at the sensor.
For more information, visit www.innatera.com.
Media Contact:
Cosmin Balan
Marketing Manager
395572@email4pr.com
215-764-7336
SOURCE Innatera
Video of Innatera’s Product Launch of Pulsar earlier today at Computex 2025:
https://lnkd.in/gMgBS2AR
And yes, it is commercially available now…
Above images are from the Press Kit: https://www.dropbox.com/scl/fo/xrpycuii9repikowerctl/AGZqj0VP0NLSuxST9CNY3Ok?
Innatera Unveils Pulsar: The World's First Mass-Market Neuromorphic Microcontroller for the Sensor Edge
/PRNewswire/ -- Innatera, a leading developer of neuromorphic processors, today announced the launch of Pulsar, its first commercially available...
Innatera Unveils Pulsar: The World's First Mass-Market Neuromorphic Microcontroller for the Sensor Edge
NEWS PROVIDED BY
InnateraMay 21, 2025, 08:59 ET
Smaller, smarter, and radically efficient - bringing brain-inspired intelligence to battery-powered devices, unlocking a new era of real-time, ultra-low power AI at the edge.DELFT, Netherlands, May 21, 2025 /PRNewswire/ --
Innatera, a leading developer of neuromorphic processors, today announced the launch of Pulsar, its first commercially available microcontroller to bring brain-like intelligence into edge devices. Born from more than a decade of pioneering research, Pulsar delivers up to 100X lower latency and 500X lower energy consumption than conventional AI processors. With this breakthrough, Innatera brings a new class of ultra-efficient, brain-inspired intelligence directly to the sensor edge.
With sensors embedded in everything from wearables and smart homes to cars and industrial systems, the need for real-time, secure, energy-efficient data processing at the edge has never been greater. Pulsar tackles this challenge head-on by processing data locally and intelligently, at the sensor level – eliminating the need to rely on brute-force compute in power-hungry edge processors or data centers to make sense of sensor data.
"Pulsar is not just another AI chip – it represents a fundamental shift in how we bring intelligence to the edge," says Sumeet Kumar, co-founder and CEO of Innatera. "This launch is the culmination of over a decade of deep research and engineering in neuromorphic computing, combined with a groundbreaking heterogeneous architecture. It marks the moment that our brain-inspired technology becomes ready for mass-market deployment. As demand for real-time, power-efficient intelligence in edge devices continues to grow, Pulsar delivers the capabilities that traditional AI hardware simply can't – ultra-low latency, minimal power draw, and on-device decision-making. More importantly, it lays the foundation for a new class of intelligent systems that are adaptive, autonomous, and scalable. Pulsar is our first major step toward making that future a reality."
Built for what's next: A platform for scalable, real-world edge intelligence
Pulsar introduces a compute architecture based on Spiking Neural Networks (SNNs), a generational leap in AI hardware that processes data the way the brain does, focusing only on changes in input. This event-driven model dramatically reduces energy use and latency while delivering precise, real-time decision-making. Pulsar goes even further by combining neuromorphic compute with traditional signal processing in a revolutionary architecture. Integrating a high-performance RISC-V CPU and dedicated accelerators for Convolutional Neural Networks (CNNs) and Fast Fourier Transform (FFT), this architecture provides exceptional versatility on a single chip.
"Innatera's Pulsar chip has the potential to redefine what's possible at the edge," says David Harold, senior analyst, Jon Peddie Research. "By using brain-inspired Spiking Neural Networks, it brings real-time processing to ultra-low-power devices without leaning on the cloud. That means sensors that can think for themselves – faster responses, lower energy use, and smarter performance across everything from wearables to industrial systems."
Smarter products, longer battery life
Pulsar gives product teams a shortcut to smarter features that were previously off-limits due to size, power, or complexity. Filtering and interpreting sensor data locally keeps the main application processor asleep until truly needed, in some cases, eliminating the need for a main application processor or cloud computing, extending battery life by orders of magnitude. With sub-milliwatt power consumption, Pulsar makes always-on intelligence truly viable, enabling everything from sub-millisecond gesture recognition in wearables to energy-efficient object detection in smart home systems. For example, it achieves real-time responsiveness with power budgets as low as 600 µW for radar-based presence detection and 400 µW for audio scene classification.
"The combination of Innatera's Spiking Neural Processor (SNP) and Socionext's highly integrated, sophisticated radar sensor technology introduces a powerful new approach to reducing power consumption and minimizing false detections in challenging applications, such as battery-powered devices," says Matthias Neumann, Senior Marketing Manager Smart Sensor & Smart Devices at Socionext. "We are confident that this collaboration will accelerate the adoption of radar sensing solutions in the market, bringing cutting-edge technology to a wider range of industries."
Simpler integration for sensor makers
Pulsar transforms traditional sensors into self-contained intelligent systems. With its small memory footprint and efficient neural models, it fits into tight form factors while eliminating the need for heavy external compute and reducing reliance on complex, custom DSP pipelines. Sensor manufacturers can now deliver plug-and-play smart sensor modules, accelerating development and time to market.
"Aria Sensing is committed to providing advanced, highly intuitive Ultra-Wideband system-on-chip and complete solutions. The Pulsar microcontroller by Innatera facilitates real-time sensing with exceptional energy efficiency, thereby creating opportunities for continuous operation applications," says Alessio Cacciatori, Founder and CEO of Aria Sensing. "We are particularly enthusiastic about the potential of Pulsar's neuromorphic architecture to integrate brain-inspired intelligence into our state-of-the-art 1D/2D/3D high-resolution sensing systems, leading to enhanced speed, responsiveness, and significantly improved power efficiency."
Empowering developers with familiar tools and a new community
Innatera's Talamo SDK makes neuromorphic development approachable. Developers can build spiking models from scratch, in a PyTorch-based environment, simulate, optimize, and deploy with ease.
To further support this ecosystem, Innatera is launching its developer program, now open to early adopters. More than just a portal, it's the foundation of a growing community designed to accelerate innovation, share knowledge, and empower members to build the next generation of intelligent edge applications together. An upcoming open-source PyTorch frontend and marketplace will create an even more collaborative ecosystem for neuromorphic AI.
"Innatera's Pulsar marks a leap forward in edge intelligence. As a partner, we see their neuromorphic approach redefining what's possible in ultra-low-power, always-on AI – exactly the kind of innovation the edge AI ecosystem needs to thrive," says Pete Bernard, CEO, EDGE AI FOUNDATION.
"As a trusted solution partner for innovative semiconductor technologies, SmartSoC is proud to collaborate with Innatera to bring cutting-edge AI solutions like Pulsar to market. Pulsar's unique neuromorphic architecture perfectly complements our mission to deliver smarter, more efficient products to customers across Europe and India, enabling a new generation of intelligent edge applications," says Bharath Desareddy, CEO, SmartSoC Solutions.
Designed to unlock the future
Beyond what Pulsar delivers today, it lays the groundwork for what's next: edge AI systems that are autonomous, adaptive, and capable of learning in the field. With each product generation, Innatera's roadmap leads toward self-calibrating, self-optimizing devices that reduce maintenance costs and unlock entirely new classes of edge applications.
Pulsar is available now. Whether you're building the next breakthrough in wearables, enabling smarter industrial systems, or designing cutting-edge sensors, Pulsar is your gateway to the future of edge intelligence.
Learn more and get started at www.innatera.com/pulsar.
For the press kit, click here.
About Innatera
Innatera is a pioneering semiconductor company at the forefront of neuromorphic computing, a revolutionary approach to AI that emulates the brain's neural networks. Its ultra-low-power processors are designed to deliver real-time, high-performance AI inference for energy-constrained applications. Founded in 2018 and headquartered in Delft, Netherlands, Innatera is backed by leading investors and collaborates with global industry leaders to redefine the future of edge AI. The company's mission is to bring cognitive capabilities to devices, enabling smarter, faster, and more efficient decision-making directly at the sensor.
For more information, visit www.innatera.com.
Media Contact:
Cosmin Balan
Marketing Manager
395572@email4pr.com
215-764-7336
SOURCE Innatera
Video of Innatera’s Product Launch of Pulsar earlier today at Computex 2025:
https://lnkd.in/gMgBS2AR
Last edited:
Baneino
Regular
Innatera's entry into the market shows the growing interest in neuromorphic hardware, especially for energy-efficient edge applications. However, this does not necessarily pose a threat to brainchip investors. Rather, it underlines the relevance and potential of neuromorphic computing as a whole.
As long as Brainchip continues to innovate and strengthen its market position, the launch of Innatera's T1 should be seen as a confirmation of market potential rather than direct competition.
Greetings from Germany
As long as Brainchip continues to innovate and strengthen its market position, the launch of Innatera's T1 should be seen as a confirmation of market potential rather than direct competition.
Greetings from Germany
Tothemoon24
Top 20
Does the secret sauce have a place here … or is it another step around our technology..
Cybersecurity, Artificial Intelligence, Security, Artificial Intelligence, Article, Trends
Kyocera’s licensing agreement with Quadric for its Chimera General-Purpose Neural Processing Unit (GPNPU) technology signals a potential acceleration of AI integration within the print industry. Quadric’s GPNPU is designed for efficient on-device AI processing, offering a blend of neural network acceleration and general-purpose computing capabilities. This licensing agreement suggests Kyocera is strategically investing in incorporating advanced AI capabilities into its future printing solutions.
The potential of integrating such on-device AI capabilities, particularly within the system-on-chip (SoC) of multifunction printers (MFPs), could be transformative. By processing AI tasks locally, MFPs could achieve:
This move could be the catalyst that kick-starts broader adoption of on-device AI across the print industry, paving the way for smarter, more efficient, and more secure print and document capture experiences.
However, although the Chimera GPNPU brings a lot of promise to the use of embedded AI, it will need an ecosystem of developers behind it to make it a success. Quadric is making the architecture as open as possible so developers will not need to learn new languages, and the use of AI itself in development (for example, through GitHub Copilot) could lead to fast delivery of AI functionality that could accelerate the adoption of AI-embedded SoC MFPs. Kyocera must also play its part, using its own reach into the developer community and the capabilities of its channel to help create a vibrant set of AI models that buyers will see as disruptive in the market. These must also provide distinct value to buyers’ document management and workflow needs.
The integration of a powerful and efficient AI processor such as Quadric’s GPNPU could unlock a range of more sophisticated applications, including:
While the specific applications and timelines for Kyocera’s implementation remain to be seen over the next two years, their licensing of Quadric’s GPNPU technology represents a potentially pivotal moment, suggesting a future where AI plays a far more central role in the print industry. It will be important to monitor Kyocera’s development progress and whether this strategic adoption indeed catalyses broader AI integration across the sector in the coming years.
As a basic idea, a printer SoC is an integrated circuit that combines all the necessary components for a printer’s functionality, or a specific area of its functionality, into a single microchip. This includes elements such as the CPU, GPU, digital signal processing (DSP), memory, power management unit (PMU), and I/O ports required by the SoC to interact with the rest of the print device all on one chip. Printer SoCs are designed to provide optimised space, power consumption, and performance in printing devices.
The use of SoCs has history the printer arena. In 2021, Fujifilm Business Innovation started using Qbit’s SoC in some of its Apeos line of MFPs. Qbit was born out of a cooperation between Fuji and Xerox. As Xerox still uses Fuji’s print engines (although Xerox’s acquisition of Lexmark gives it access to a level of in-house designed and built engines), its SoC capabilities are likely to be similar to Fujifilm Business Innovation’s for a while.
Also in 2021, Kyocera Document Solutions signed an agreement with Synopsys to use its DesignWare ARC EV6x Embedded Vision Processor IP with its convolutional neural network (CNN) engine and ARC MetaWare EV development toolkit. As part of this, Kyocera also put in place the tools to create an AI ecosystem using Synopsys’ HAPS FPGA-based prototyping system.
In 2022, Konica Minolta progressed its dual chip CPU/ASIC design to being a single SoC approach, providing an upgradable platform.
With Kyocera’s adoption of GPNPU technology, print vendors now have the potential to reimagine their embedded AI strategies. If successful, this could accelerate the industry’s shift towards smarter, self-optimising print ecosystems. However, with each print OEM seemingly going its own way, creating the necessary vibrant ecosystem around print device AI may be difficult – the same problem that the tech industry has faced repeatedly. Without some standardised way of creating viable solutions that are portable across devices, users may find sticking with device-independent capabilities in the cloud more appealing.
Quocirca’s research suggests that print industry AI adoption will gain momentum if early implementations demonstrate tangible business value, efficiency gains, and cost benefits. While it remains to be seen whether this and other OEM-led AI development will catalyse broader AI adoption across the industry, it undoubtedly raises the bar for intelligent print solutions. If Kyocera can demonstrate clear efficiency gains, competitors may be compelled to fast-track their own AI-driven initiatives, moving away from multi-chip and cloud-based solutions to a more centralised GPNPU SoC approach, accelerating the evolution of the print sector into a more dynamic, data-driven ecosystem.
To find out more about how print vendors are applying AI to their products, read Quocirca’s AI Vendor Landscape 2025 Study
To find out more about becoming a Quocirca client, click here.
quocirca.com
Kyocera’s licensing of Quadric’s GPNPU could kick-start on-device print AI
May 21, 2025Cybersecurity, Artificial Intelligence, Security, Artificial Intelligence, Article, Trends
Kyocera’s licensing agreement with Quadric for its Chimera General-Purpose Neural Processing Unit (GPNPU) technology signals a potential acceleration of AI integration within the print industry. Quadric’s GPNPU is designed for efficient on-device AI processing, offering a blend of neural network acceleration and general-purpose computing capabilities. This licensing agreement suggests Kyocera is strategically investing in incorporating advanced AI capabilities into its future printing solutions.
What is Quadric’s Chimera GPNPU?
Quadric’s Chimera GPNPU is a licensable processor that scales from 1 to 864 tera operations per second (TOPS). Chimera GPNPUs run all machine learning (ML) networks – including classical backbones, vision transformers, and large language models (LLMs). Quadric’s Chimera architecture represents a novel approach to on-device AI processing, uniquely integrating the high-throughput parallel processing capabilities essential for neural network acceleration with the versatility of a general-purpose processor within a unified silicon architecture. This design allows the Chimera GPNPU to efficiently handle a diverse range of AI inference tasks and traditional computing functions on the device itself, without constant reliance on cloud connectivity.The potential of integrating such on-device AI capabilities, particularly within the system-on-chip (SoC) of multifunction printers (MFPs), could be transformative. By processing AI tasks locally, MFPs could achieve:
- Enhanced speed and responsiveness. Real-time analysis and decision-making for tasks such as image optimisation or document classification can be carried out within the device itself, without the latency associated with cloud communication.
- Improved security and privacy. Sensitive document content and user data can be processed and analysed locally, reducing the need to transmit information to external servers and mitigating potential security risks. Decisions can be made on the device in areas such as data classification, data leak prevention (DLP), and information redaction without any information leaving the enhanced security of the devices itself.
- Greater reliability and autonomy. As well as the simple print, scan, and finishing capabilities of the MFP itself, the advanced functionality brought via AI can remain available even without a stable internet connection, enhancing the reliability of intelligent features.
- Reduced operational costs. Although it is likely to have fewer cost savings, minimising any reliance on cloud processing can lead to lower data transmission and cloud service fees.
- New intelligent features. Embedded AI can help by enabling more complex on-device functionalities such as advanced document understanding, proactive security threat detection within print streams, personalised user interfaces driven by usage patterns, and intelligent workflow automation embedded directly within the MFP.
Is Kyocera ahead of the game?
While the specific implementation and timelines for Kyocera’s integration of the Chimera GPNPU remain to be seen – with the actual SoC and silicon expected to take approximately two years to materialise – this licensing agreement signals a clear intent to leverage the power of on-device AI. By indicating plans to embed Quadric’s innovative GPNPU technology directly into their MFPs, Kyocera is positioning itself to potentially lead the charge in developing a new generation of intelligent AI-enabled MFPs that offer enhanced performance, security, and document automation.This move could be the catalyst that kick-starts broader adoption of on-device AI across the print industry, paving the way for smarter, more efficient, and more secure print and document capture experiences.
However, although the Chimera GPNPU brings a lot of promise to the use of embedded AI, it will need an ecosystem of developers behind it to make it a success. Quadric is making the architecture as open as possible so developers will not need to learn new languages, and the use of AI itself in development (for example, through GitHub Copilot) could lead to fast delivery of AI functionality that could accelerate the adoption of AI-embedded SoC MFPs. Kyocera must also play its part, using its own reach into the developer community and the capabilities of its channel to help create a vibrant set of AI models that buyers will see as disruptive in the market. These must also provide distinct value to buyers’ document management and workflow needs.
Market implications
Traditionally, AI in print has been largely confined to predictive maintenance and security enhancements, but Quadric’s GPNPU technology could expand its reach into real-time document analysis and intelligent data processing. With businesses handling ever-growing volumes of structured and unstructured data, AI-powered print solutions could streamline document classification, enhance compliance automation, and improve security through automatic fault resolution and intelligent redaction and anomaly detection.The integration of a powerful and efficient AI processor such as Quadric’s GPNPU could unlock a range of more sophisticated applications, including:
- Advanced image analysis and enhancement. AI can provide a large improvement in the real-time optimisation of print quality based on image content, potentially leading to reduced waste and improved output.
- Intelligent document processing. The use of AI around automated document classification, data extraction, and workflow optimisation directly within the printing device can provide distinct business value to the user.
- Enhanced security features. AI-powered threat detection and prevention within the printing environment, protecting sensitive information, can help provide the additional security that print devices have historically lacked.
- Personalised printing experiences. Print output and workflows can be tailored to individual user, group, and organisation preferences and needs.
- Predictive maintenance and resource management. Sophisticated AI algorithms can aid in better predicting maintenance needs, optimising energy consumption, and managing supplies. This can help reduce the need for on-site engineer visits, while just-in-time (JiT) consumables management can reduce OEM/channel inventories and customer storage needs.
While the specific applications and timelines for Kyocera’s implementation remain to be seen over the next two years, their licensing of Quadric’s GPNPU technology represents a potentially pivotal moment, suggesting a future where AI plays a far more central role in the print industry. It will be important to monitor Kyocera’s development progress and whether this strategic adoption indeed catalyses broader AI integration across the sector in the coming years.
SoC architectures in MFPs
Other OEMs have been using an SoC approach for certain functionality in the past, and are now building on this to create on-device or hybrid AI platforms.As a basic idea, a printer SoC is an integrated circuit that combines all the necessary components for a printer’s functionality, or a specific area of its functionality, into a single microchip. This includes elements such as the CPU, GPU, digital signal processing (DSP), memory, power management unit (PMU), and I/O ports required by the SoC to interact with the rest of the print device all on one chip. Printer SoCs are designed to provide optimised space, power consumption, and performance in printing devices.
The use of SoCs has history the printer arena. In 2021, Fujifilm Business Innovation started using Qbit’s SoC in some of its Apeos line of MFPs. Qbit was born out of a cooperation between Fuji and Xerox. As Xerox still uses Fuji’s print engines (although Xerox’s acquisition of Lexmark gives it access to a level of in-house designed and built engines), its SoC capabilities are likely to be similar to Fujifilm Business Innovation’s for a while.
Also in 2021, Kyocera Document Solutions signed an agreement with Synopsys to use its DesignWare ARC EV6x Embedded Vision Processor IP with its convolutional neural network (CNN) engine and ARC MetaWare EV development toolkit. As part of this, Kyocera also put in place the tools to create an AI ecosystem using Synopsys’ HAPS FPGA-based prototyping system.
In 2022, Konica Minolta progressed its dual chip CPU/ASIC design to being a single SoC approach, providing an upgradable platform.
With Kyocera’s adoption of GPNPU technology, print vendors now have the potential to reimagine their embedded AI strategies. If successful, this could accelerate the industry’s shift towards smarter, self-optimising print ecosystems. However, with each print OEM seemingly going its own way, creating the necessary vibrant ecosystem around print device AI may be difficult – the same problem that the tech industry has faced repeatedly. Without some standardised way of creating viable solutions that are portable across devices, users may find sticking with device-independent capabilities in the cloud more appealing.
Quocirca opinion
Kyocera’s GPNPU licensing as an important milestone. Its move towards GPNPU-based AI is a key indicator that print manufacturers are recognising the strategic importance of AI-driven workflow automation. The ability to process documents more intelligently – whether through automated classification, metadata extraction, or security enhancements – could reshape enterprise print strategies, as long as buyers can see the value in such an approach. While AI adoption in the print sector has historically been cautious, the integration of GPNPU technology could push competitors to accelerate innovation in embedded AI solutions.Quocirca’s research suggests that print industry AI adoption will gain momentum if early implementations demonstrate tangible business value, efficiency gains, and cost benefits. While it remains to be seen whether this and other OEM-led AI development will catalyse broader AI adoption across the industry, it undoubtedly raises the bar for intelligent print solutions. If Kyocera can demonstrate clear efficiency gains, competitors may be compelled to fast-track their own AI-driven initiatives, moving away from multi-chip and cloud-based solutions to a more centralised GPNPU SoC approach, accelerating the evolution of the print sector into a more dynamic, data-driven ecosystem.
To find out more about how print vendors are applying AI to their products, read Quocirca’s AI Vendor Landscape 2025 Study
To find out more about becoming a Quocirca client, click here.
Kyocera's licensing of Quadric's GPNPU could kick-start on-device print AI - Quocirca Quocirca
Kyocera's recent licensing of Quadric's Chimera (GPNPU) technology could mean acceleration of AI integration within the print industry.
quocirca.com
Pom down under
Top 20
FJ-215
Regular
Similar threads
