I am not saying you are definitely wrong but Anil Mankar likes are problematic because he is so well known in the industry and respected. He is just a genuine nice person so his likes and interest in others is not always about Brainchip.
My opinion only DYOR
FF
AKIDA BALLISTA
Some more research indicates that STMicroelectronics are attempting to do neuromorphic computing with RRAM.
In an article in February, 2022 STM mentioned that neuromorphic chips are not mature enough. They want to move computation to the memory.
In the plenary session of the ISSCC conference, Marco Cassis, president of ST’s Analog, MEMS and Sensors Group, looked at the various AI technologies for sensors. He rules out spiking neural network chips, also called neuromorphic, as not mature, saying current convolutional neural networks can tap into reduced precision and semiconductor scaling to get more performance. However these CNN devices struggle with power consumption and memory bandwidth challenges that get in the way of scalability.
“To overcome this limitation is to partially or completely move the computation to the memory,” he said. “In Memory Computing can bring big benefits, 100x densities and efficiencies compared to current state of the art solutions. Here an especially promising avenue is the use of non volatile resistive memory devices to perform computations in the memory itself.
STMicroelectronics is looking at the development of an AI chip using analog in-memory computing with its resistive phase change memory. In the plenary session of the ISSCC conference, Marco Cassis, president of ST’s Analog, MEMS and Sensors Group, looked at the various AI technologies for...
www.eenewseurope.com
In May, 2019 to January 2023 a consortium of companies including STM & European Commission got together for the TEMPO project.
Technology and hardware for neuromorphic computing
Project description
New ways to integrate emerging memories to enable neuromorphic computing systems
Artificial intelligence (AI) and machine learning are used today for computing all kinds of data, making predictions and solving problems. These are processes based increasingly on deep neuronal network (DNN) models. As the volume of produced data slow down machines and consume greater amounts of energy, there is a new generation of neural units. The spiking neural networks (SNNs) incorporate biologically-feasible spiking neurons with their temporal dynamics. The EU-funded TEMPO project will leverage emerging memory technology to design new innovative technological solutions that make data integration simpler and easier via new neuronal DNN and SNN computing engines. Reduced core computational operational systems’ neuromorphic algorithms will serve as demonstrators.
Objective
Massive adoption of computing in all aspects of human activity has led to unprecedented growth in the amount of data generated. Machine learning has been employed to classify and infer patterns from this abundance of raw data, at various levels of abstraction. Among the algorithms used, brain-inspired, or “neuromorphic”, computation provides a wide range of classification and/or prediction tools. Additionally, certain implementations come about with a significant promise of energy efficiency: highly optimized Deep Neural Network (DNN) engines, ranging up to the efficiency promise of exploratory Spiking Neural Networks (SNN). Given the slowdown of silicon-only scaling, it is important to extend the roadmap of neuromorphic implementations by leveraging fitting technology innovations. Along these lines, the current project aims to sweep technology options, covering emerging memories and 3D integration, and attempt to pair them with contemporary (DNN) and exploratory (SNN) neuromorphic computing paradigms. The process- and design-compatibility of each technology option will be assessed with respect to established integration practices. Core computational kernels of such DNN/SNN algorithms (e.g. dot-product/integrate-and-fire engines) will be reduced to practice in representative demonstrators.
Some other well known companies involved are Valeo, Phillips, Thales, Bosch, Infineon & SynSense.
Massive adoption of computing in all aspects of human activity has led to unprecedented growth in the amount of data generated. Machine learning has been employed to classify and infer patterns from this abundance of raw data, at various levels of abstraction. Among the...
cordis.europa.eu
In June, 2022 STM released new inertial sensors containing the intelligent sensor processing unit (ISPU) for on device processing.
The ISM330IS embeds a new ST category of processing, ISPU (intelligent sensor processing unit) to support real-time applications that rely on sensor data. The ISPU is an ultra-low-power, high-performance programmable core which can execute signal processing and AI algorithms in the edge. The main benefits of the ISPU are C programming and an enhanced ecosystem with libraries and 3rd party tools/IDE.
The
ISM330ISN is scheduled to enter production in H2 2022 and will be available from
st.com or distributors for $3.48 for orders of 1000 pieces. NanoEdge AI Studio enabling the creation of libraries designed for specific ISPU part numbers is available at no charge on ST.com
Due to the release date the ISPU is unlikely to have Akida IP. As neuromorphic hardware becomes available & matures they may be interested.
Geneva, June 16, 2022 – STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, has introduced new inertial sensors that con…
www.eejournal.com
STM has a big ecosystem & 200,000 customers.
An integrated device manufacturer, we work with more than 200,000 customers and thousands of partners to design and build products, solutions, and ecosystems that address their challenges and opportunities, and the need to support a more sustainable world. Our technologies enable smarter mobility, more efficient power and energy management, and the wide-scale deployment of the Internet of Things and connectivity.
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www.linkedin.com
AU$360.00 per share!!
stm32ai.st.com
