VITORIA-GASTEIZ, Spain--(BUSINESS WIRE)--Acceleration Robotics —a robotics semiconductor startup based in the Basque Country, Spain— together with Prof. Vijay Janapa Reddi from Harvard University and ...

“Industry has gradually moved towards application-specific hardware accelerators in order to attain higher efficiency. While such a paradigm shift is already starting to show promising results, ...

New work in robotics research at MIT suggests that long-term bottlenecks in robot responsiveness could be alleviated through the use of dedicated hardware accelerators. The research team also suggests ...

A major design challenge facing numerous silicon design teams in 2023 is building the right amount of machine learning (ML) performance capability into today’s silicon tape out in anticipation of what ...

Hardware accelerators — specialized devices used to perform specific tasks like classifying objects — are increasingly embedded into system-on-chips (SoCs) serving various AI applications. They help ...

The rule of thumb in embedded system design has been that addinghardware increases power demands. The careful use of hardwareaccelerators, however, inverts the rule: adding hardware can reducepower.

As AI-accelerated workloads proliferate across edge environments—from smart cities to retail and industrial surveillance—choosing the right inference accelerator has become a mission-critical decision ...

While the disruptive nature of AI is clearly seen across industries, there are still many challenges, including increasing workloads and inherent energy consumption. Given deep learning and big data ...

Recurrent neural networks (RNNs), and in particular long short-term memory (LSTM) variants, have become indispensable for sequential data processing tasks such as speech recognition, natural language ...

Dedicated accelerator hardware for artificial intelligence and machine learning (AI/ML) algorithms are increasingly prevalent in data centers and endpoint devices. These accelerators handle valuable ...