Aspinity’s Analog Neural Net Wake-Up Call


Putting an analog chip in front of an always-on system for digitizing speech and having the analog chip listen for sounds of interest may help avoid huge power waste and data congestion in current voice-recognition systems. Aspinity, an analog neuromorphic semiconductor startup, has worked the problem and just announced its Reconfigurable Analog Modular Processor (RAMP) platform yesterday. RAMP... » read more

AiMotive Is EDA For Self-Driving Cars


The team at aiMotive, a tool and IP company for OEMs making automated vehicles, isn’t waiting for smart infrastructure or 5G to make self-driving cars possible. The four-year-old startup based in Budapest, Hungary, is taking a self-sustainable route for the foreseeable future. The key to staying in business is not to compete with Waymo, Cruise or automotive companies, but to build the softwar... » read more

Machine Learning on Arm Cortex-M Microcontrollers


Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing Neural Network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. CMSIS-NN is an open-source library of... » read more

Enabling Embedded Vision Neural Network DSPs


Neural networks are now being developed in a variety of technology segments in the embedded market, from mobile to surveillance to the automotive segment. The computational and power requirements to process this data is increasing, with new methods to approach deep learning challenges emerging every day. Vision processing systems must be designed holistically, for all platforms, with hardwa... » read more

AI Architectures Must Change


Using existing architectures for solving machine learning and artificial intelligence problems is becoming impractical. The total energy consumed by AI is rising significantly, and CPUs and GPUs increasingly are looking like the wrong tools for the job. Several roundtables have concluded the best opportunity for significant change happens when there is no legacy IP. Most designs have evolved... » read more

System Bits: July 16


Test tube AI neural network In a significant step towards demonstrating the capacity to program artificial intelligence into synthetic biomolecular circuits, Caltech researchers have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: correctly identifying handwritten numbers. The work was done in the laboratory of Lulu Qian, assistant p... » read more

System Bits: May 8


Unlocking the brain Stanford University researchers recently reminded that for years, the people developing artificial intelligence drew inspiration from what was known about the human brain, and now AI is starting to return the favor: while not explicitly designed to do so, certain AI systems seem to mimic our brains’ inner workings more closely than previously thought. [caption id="attach... » read more

System Bits: Aug. 15


Machine-learning system for smoother streaming To combat the frustration of video buffering or pixelation, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “Pensieve,” an artificial intelligence system that uses machine learning to pick different algorithms depending on network conditions thereby delivering a higher-quality streaming exp... » read more

System Bits: July 18


Melanoma predicted from images with a high degree of accuracy by neural network model The poke and punch of traditional melanoma biopsies could be avoided in the near future, thanks to work by UC Santa Barbara researchers. UCSB undergrad Abhishek Bhattacharya is using the power of artificial intelligence to help people ascertain whether that new and strange mark is, in fact, the deadly skin... » read more

System Bits: June 13


Nimble-fingered robots enabled by deep learning Grabbing awkwardly shaped items that humans regularly pick up daily is not so easy for robots, as they don’t know where to apply grip. To overcome this, UC Berkeley researchers have a built a robot that can pick up and move unfamiliar, real-world objects with a 99% success rate. Berkeley professor Ken Goldberg, postdoctoral researcher Jeff M... » read more

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