I’ve certainly voted yes to everything, except the spill (item 9). I believe a second strike and spill would be an absolute disaster for the company and the momentum going on behind the scenes. Potential customers might think again and our credibility would be tarnished. It certainly wouldn’t help those complaining about how slowly things are going - quite the opposite I would think. My 5 cents worth anyway…
Wow, thanks Esq for that info.
Thanks Diogenes, your input is of great value to me and many others.Hi SS,
Looks like Tianjic uses analog NN for audio. It also uses CNN (MAC-based) for video and something else for control.
A hybrid and scalable brain-inspired robotic platform | Scientific Reports (nature.com)
A hybrid and scalable brain-inspired robotic platform - Scientific Reports
Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further...www.nature.com
View attachment 61361
For each module, we opened independent data paths, which were distinct in information representation, frequency and throughput. In the visual module, each frame of video was resized to a 70 × 70 gray image and fed into a CNN as multi-bit values, enabling rich environmental spatial information to be maintained with limited computing resources (Fig. 3a). In contrast, the raw audio stream was transformed into binary spike trains in the auditory module. After end-point detection32,33, the key frequency features were obtained by taking the Mel Frequency Cepstral Coefficient (MFCC)34. A Gaussian population35 was used to encode each MFCC feature into spike trains as input to a three-layer fully-connected SNN (Fig. 3b). For motion control, sequential signals were first generated by functional cores to merge with the steering commands from other modules as the comprehensive target angle. Data from all sensors were then integrated via the MLP network for angle control (Fig. 3d). The designs and training of each network-based module are described in the Methods.
Tianjic uses different technologies to handle visual and audio. Akida can handle both.
They use 1-bit analog for audio which is very efficient but not so hot on accuracy. Akida can do 1-bit up to 8-bit inputs (256 values).
View attachment 61363
Tianjic needs a separate FPGA for pre-processing input signals. Akida has built-in preprocessing.
So, competition? - Yes.
Worried? - No,
I saw the movie several times but I guess I didn’t catch it Because it was synchronised in German language.It's all good friend.
I appreciate the cross pollination that happens here when different cultures, skill sets compulsions and languages mix and blend.
It's groovy.
Good morning Dingo....
Sometimes it's hard to see past the forest because of all those trees !
Robot Ken is simply telling us that he's in France, most likely, Toulouse, where our brilliant software team has been working really
hard on his software makeup, as we all know the fastest process is the hardware phase, whereas the software is the much slower,
time consuming phase, the hint is, whom are we working in with in the robotics world.
But as usual, that's just my view...
I will let you read on and not spoil the big surprise.Looks like wet wipes to me.
I find this all very exciting. They let us guess what is happening here.
I think that something big is coming up.
Ooookayyy, it is Rice CrispiesI will let you read on and not spoil the big surprise.
, but nevertheless something big is coming 
I’m going to hold off for now voting, just incase the company can change my mind beforehand.
I normally vote beforehand, now I'm confused
They surely seems to be a competitor (unless they are using Alkida).@Diogenese,
This recent patents from Syntiant using co neuromorphic processor, is it anything of interest here?
US20240062056A1 - Offline Detector - Google Patents
Provided herein is an integrated circuit including, in some embodiments, a special-purpose host processor, a neuromorphic co-processor, and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The special-purpose host...patents.google.com
Abstract
Provided herein is an integrated circuit including, in some embodiments, a special-purpose host processor, a neuromorphic co-processor, and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The special-purpose host processor can be operable as a stand-alone processor. The neuromorphic co-processor may include an artificial neural network. The co-processor is configured to enhance special-purpose processing of the host processor through an artificial neural network. In such embodiments, the host processor is a pattern identifier processor configured to transmit one or more detected patterns to the co-processor over a communications interface. The co-processor is configured to transmit the recognized patterns to the host processor.
And the new release NDP250.
Amir Sherman on LinkedIn: Syntiant Unveils NDP250 Neural Decision Processor with Next-Gen Core 3…
Syntiant Corp. just announced the NDP250 Neural Decision Processor with Next-Gen Core 3 Architecture, and Edge Impulse is excited to start working on it to win…www.linkedin.com
Moving Vision AI from the Cloud to the Device
The NDP250 has advanced image capabilities and is ideal for ultra-low power video applications for automotive security, battery-powered cameras and video doorbells. Running powerful always-on image recognition at under 30mW has several advantages, including:
A significant reduction in power consumption by processing data locally on devices, thereby extending battery life and enabling efficient resource utilization.
Lower latency since data doesn't need to travel back and forth to a remote server, resulting in faster response times crucial for real-time applications and greatly increases customer satisfaction.
Enhanced privacy by processing sensitive data locally, minimizing the need to transmit information over networks where it could be vulnerable to breaches or interception.
A notable reduction in cloud costs, sometimes as high as 90%, since less data needs to be transferred and processed in the cloud, leading to lower infrastructure expenses for businesses deploying edge AI solutions.
Equipped with an Arm Cortex M0 processor and a HiFi 3 DSP to support feature extraction and signal processing for image and voice enhancements, the NDP250’s integrated power management unit allows single power rail operation, where the integrated phase-locked Loop (PLL) provides further system cost and size optimization.
With the ability to process multiple heterogenous networks concurrently, the NDP250 also supports convolution neural networks including 1D, 2D and depth-wise, fully connected networks, and recurrent neural networks including LSTM (long short-term memory) and GRU (gated recurrent unit).
Other key features include:
Syntiant Core 3 neural network
Supports more than 6M neural parameters (in 8-bit mode)
Hardware acceleration over 30 GOPS
Dual 11-wire direct image interface
Dual PDM digital microphone interface
I2S serial interface with PCM
Quad-SPI and dual-I2C controller and target for multi-modal sensor fusion
Up to 120MHz internal operating frequency
Low power PLL for flexible clock input
Software Development Kit
Training Development Kit
120-ball 6.1mm x 5.1mm eWLB package (0.5mm pitch)
The NDP250 is sampling now.
Syntiant will be demonstrating the NDP250 and various hardware-agnostic deep learning vison models at Embedded World 2024 (Hall 2 - Booth 2-338), April 9-11 in Nuremberg, Germany. Contact info@syntiant.com to arrange a meeting or demo at Embedded World.
Anything to see here?
Learning
www.syntiant.com
www.syntiant.com
www.syntiant.com
Looks a good competitor look at their partner listsThey surely seems to be a competitor (unless they are using Alkida).
Quote:
A New Kind of Processor for Deep Learning
Our Neural Decision Processors™ enable customers to quickly and easily deploy deep-learning models on power-constrained devices that previously ran on cloud servers.
| Provide 100x the efficiency and 10/30x higher throughput when compared to existing low-power MCUs.
| At-memory compute greatly reduces power consumption and latency by eliminating any unnecessary data movement.
| Specially designed to run deep-learning models, directly processing neural network layers achieving unprecedented levels of efficiency, often greater than 80%.
| Multi-generational product offerings provide scalability for any edge workload.
Syntiant
They seems to be selling a lot we are involved in, which is quite worrying.
They already have mass production of different sensors as per link below, and no, I don't think you have to pay 1 Mill$ to get hold of them.
Hardware — Syntiant
They also sells evaluation kits from Renesas and others for under 100$ a pop.
I really hope we are involved here.
Partners:
Partner Network — Syntiant
Syntiant are very analoggy:@Diogenese,
This recent patents from Syntiant using co neuromorphic processor, is it anything of interest here?
US20240062056A1 - Offline Detector - Google Patents
Provided herein is an integrated circuit including, in some embodiments, a special-purpose host processor, a neuromorphic co-processor, and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The special-purpose host...patents.google.com
Abstract
Provided herein is an integrated circuit including, in some embodiments, a special-purpose host processor, a neuromorphic co-processor, and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The special-purpose host processor can be operable as a stand-alone processor. The neuromorphic co-processor may include an artificial neural network. The co-processor is configured to enhance special-purpose processing of the host processor through an artificial neural network. In such embodiments, the host processor is a pattern identifier processor configured to transmit one or more detected patterns to the co-processor over a communications interface. The co-processor is configured to transmit the recognized patterns to the host processor.
And the new release NDP250.
Amir Sherman on LinkedIn: Syntiant Unveils NDP250 Neural Decision Processor with Next-Gen Core 3…
Syntiant Corp. just announced the NDP250 Neural Decision Processor with Next-Gen Core 3 Architecture, and Edge Impulse is excited to start working on it to win…
Moving Vision AI from the Cloud to the Device
The NDP250 has advanced image capabilities and is ideal for ultra-low power video applications for automotive security, battery-powered cameras and video doorbells. Running powerful always-on image recognition at under 30mW has several advantages, including:
A significant reduction in power consumption by processing data locally on devices, thereby extending battery life and enabling efficient resource utilization.
Lower latency since data doesn't need to travel back and forth to a remote server, resulting in faster response times crucial for real-time applications and greatly increases customer satisfaction.
Enhanced privacy by processing sensitive data locally, minimizing the need to transmit information over networks where it could be vulnerable to breaches or interception.
A notable reduction in cloud costs, sometimes as high as 90%, since less data needs to be transferred and processed in the cloud, leading to lower infrastructure expenses for businesses deploying edge AI solutions.
Equipped with an Arm Cortex M0 processor and a HiFi 3 DSP to support feature extraction and signal processing for image and voice enhancements, the NDP250’s integrated power management unit allows single power rail operation, where the integrated phase-locked Loop (PLL) provides further system cost and size optimization.
With the ability to process multiple heterogenous networks concurrently, the NDP250 also supports convolution neural networks including 1D, 2D and depth-wise, fully connected networks, and recurrent neural networks including LSTM (long short-term memory) and GRU (gated recurrent unit).
Other key features include:
Syntiant Core 3 neural network
Supports more than 6M neural parameters (in 8-bit mode)
Hardware acceleration over 30 GOPS
Dual 11-wire direct image interface
Dual PDM digital microphone interface
I2S serial interface with PCM
Quad-SPI and dual-I2C controller and target for multi-modal sensor fusion
Up to 120MHz internal operating frequency
Low power PLL for flexible clock input
Software Development Kit
Training Development Kit
120-ball 6.1mm x 5.1mm eWLB package (0.5mm pitch)
The NDP250 is sampling now.
Syntiant will be demonstrating the NDP250 and various hardware-agnostic deep learning vison models at Embedded World 2024 (Hall 2 - Booth 2-338), April 9-11 in Nuremberg, Germany. Contact info@syntiant.com to arrange a meeting or demo at Embedded World.
Anything to see here?
Learning
It's true that we have not seen any published details of engagements, but it may be that there are some behind the NDA wall.
