Hardware

Neural network pruning is a key technique for deploying artificial intelligence (AI) models based on deep neural networks (DNNs) on resource-constrained platforms, such as mobile devices. However, hardware conditions and resource availability vary greatly across different platforms, making it essential to design pruned models optimally suited to specific hardware configurations.... Read more

Ever wonder what happens to massive supercomputing systems when they're retired? Surprisingly, when it comes to the data, it's not too different from disposing of old documents—they go straight into a shredder and sent to recycling.... Read more

Online gaming is increasingly popular. As such, server efficiency is becoming an increasingly urgent priority. With millions of players interacting in real-time, game servers are under enormous pressure to process a huge amount of data without latency (delays) or crashes. Research in the International Journal of Information and Communication Technology... Read more

Researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform capable of storing and processing data in an astonishing 16,500 conductance states within a molecular film. Published today in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which... Read more

Imagine a future where internet connections are not only lightning-fast but also remarkably reliable, even in crowded spaces. This vision is rapidly approaching reality, thanks to new research on terahertz communications technologies. These innovations are set to transform wireless communication, particularly as communications technology advances toward the next generation of... Read more

A team of scientists has unlocked the potential of 6G communications with a new polarization multiplexer. Terahertz communications represent the next frontier in wireless technology, promising data transmission rates far exceeding current systems.... Read more

Researchers from EPFL have developed a next-generation miniaturized brain-machine interface capable of direct brain-to-text communication on tiny silicon chips.... Read more

Over the past couple of decades, computer scientists have developed a wide range of deep neural networks (DNNs) designed to tackle various real-world tasks. While some of these models have proved to be highly effective, some studies found that they can be unfair, meaning that their performance may vary based... Read more

Air quality has become one of the most important public health issues in Africa. Poor air quality kills more people globally every year than HIV, TB and malaria combined. And that's just the tip of the iceberg. Air pollution makes people less productive because they get headaches and feel tired.... Read more

In 2021, a driver in Albuquerque, New Mexico, ran a red light, striking and killing a 7-year-old and injuring his father. The suspect fled the scene and eventually escaped to Mexico. Using camera footage and cellphone data, the Albuquerque Police Department's real-time crime center played a crucial role in identifying,... Read more

The recent widespread and long-lasting chaos caused by Microsoft outages across the globe exemplifies just how integral computing has become to our lives. Yet, as computer hardware and software improve, arguably the most sophisticated and powerful computer we know of is still the human brain.... Read more

Engineering researchers at the University of Minnesota Twin Cities have demonstrated a state-of-the-art hardware device that could reduce energy consumption for artificial intelligent (AI) computing applications by a factor of at least 1,000.... Read more

Large-scale neural network models form the basis of many AI-based technologies such as neuromorphic chips, which are inspired by the human brain. Training these networks can be tedious, time-consuming, and energy-inefficient given that the model is often first trained on a computer and then transferred to the chip. This limits... Read more

A 28GHz time-division multiple-input multiple-output (MIMO) receiver with eight radio frequency elements, each occupying just 0.1 mm2, has been developed by researchers at Tokyo Tech using 65nm CMOS technology. This innovative design reduces chip size for beamforming. Achieving -23.5 dB error vector magnitude in 64-quadrature amplitude modulation and data rates... Read more

A new D-band CMOS transceiver chipset with 56 GHz signal-chain bandwidth achieves the highest transmission speed of 640 Gbps for a wireless device realized with integrated circuits, as reported by researchers from Tokyo Tech and National Institute of Information and Communications Technology. The proposed chipset is highly promising for the... Read more