Machine learning-based applications have become prevalent across consumer, medical, and automotive markets. Still, the underlying architecture(s) and implementations are evolving rapidly, to best fit the throughput, latency, and power efficiency requirements of an ever increasing application space. Although ML is … Read More
I’ve been following IBM’s AI efforts with interest for a quite a while now. In my opinion, the company jump-started the current cycle of AI with the introduction of Watson back in the 2000s and has steadily been ramping up its efforts since then. Most recently, I wrote about the launch of PowerAI, IBM’s software toolkit solution to… Read More
Convolutional Neural Network (CNN) recognition algorithm is generating very high interest in the semi industry. At first, because CNN provides best recognition quality when compared with alternative recognition algorithms. CEVA Deep Neural Network (CDNN) software framework, implemented with CEVA-XM4 imaging & vision… Read More
My first brush with AI was a LISP class for my undergraduate degree. LISP, originated from MIT in 1958, was the language of choice for AI research and spawned a new class of computer hardware called LISP Machines in the 1980s. My first personal experience with AI was the HAL 9000 system from the 2001 Stanley Kubrik movie Space Odyssey.… Read More
Research on deep learning and convolutional neural networks (CNNs) is on the rise – and embedding new algorithms is drawing more attention. At CVPR 2016, CEVA is launching their 2[SUP]nd[/SUP] generation Deep Neural Network (CDNN2) software with new support for Google TensorFlow.… Read More
Every day I read stuff about the coming of fully autonomous vehicles, and it’s not every day we get a technologist’s view of the hurdles faced in getting there. Chris Rowen, CTO of Cadence’s IP group, gave one of the best presentations I’ve seen on ADAS technology and convolutional neural networks (CNNs) at #53DAC, pointing… Read More
