System Bits: Sept. 18


Better AI technique for chemistry predictions CalTech researchers have found a new technique that uses machine learning more effectively to predict how complex chemicals will react to reagents. The tool is a new twist on similar machine learning techniques to find more effective catalysts without having the time-consuming trial-and-error research, making it a time-saver for drug researchers. ... » read more

Variation In Low-Power FinFET Designs


One of the biggest advantages of moving to the a leading edge process node is ultra-low voltage operation, where devices can achieve better performance using less power. But the latest generation process nodes also introduce a number of new challenges due to increased variation that can affect everything from signal integrity to manufacturing yield. While variation is generally well understo... » read more

System Bits: Aug. 21


Two types of computers create faster, less energy-intensive image processor for autonomous cars, security cameras, medical devices Stanford University researchers reminded that the image recognition technology that underlies today’s autonomous cars and aerial drones depends on artificial intelligence. These are the computers that essentially teach themselves to recognize objects like a dog, ... » read more

System Bits: Aug. 14


Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors In an effort to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer, MIT researchers are employing novel machine-learning techniques. According to the team, glioblastoma is a malignant tumor ... » 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

More Processing Everywhere


Simon Segars, CEO of Arm Holdings, sat down with Semiconductor Engineering to discuss security, power, the IoT, a big push at the edge, and the rise of 5G and China. What follows are excerpts of that conversation. SE: Are we making any progress in security? And even if Arm makes progress, does it matter, given there are so many things connected together? Segars: It feels like we’re maki... » read more

Pace Quickens As Machine Learning Moves To The Edge


Artificial intelligence applications are rapidly changing the way society engages with technology. It wasn’t too long ago that your smart phone couldn’t recognize your face or your thumbprint. It also wasn’t too long ago that Alexa wasn’t helping you navigate your day so easily. And not too long ago, odds are, you weren’t developing an application or device that had AI/ML as its ce... » read more

Faster Verification With AI, ML


Tool providers have continually improved the performance, capacity, and memory footprint parameters of functional verification engines over the past decade. Today, although the core anchors are still formal verification, simulation, emulation, and FPGA-based prototyping, a new frontier focusing on the verification fabric itself aims to make better use of these engines including planning, alloca... » read more

Architecting For AI


Semiconductor Engineering sat down to talk about what is needed today to enable artificial intelligence training and inferencing with Manoj Roge, vice president, strategic planning at Achronix; Ty Garibay, CTO at Arteris IP; Chris Rowen, CEO of Babblelabs; David White, distinguished engineer at Cadence; Cheng Wang, senior VP engineering at Flex Logix; and Raik Brinkmann, president and CEO of O... » 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

← Older posts