Using Machine Learning In EDA

Machine learning is beginning to have an impact on the EDA tools business, cutting the cost of designs by allowing tools to suggest solutions to common problems that would take design teams weeks or even months to work through. This reduces the cost of designs. It also potentially expands the market for EDA tools, opening the door to even new design starts and more chips from more compan... » read more

The Efficiency Problem

Part one of this report addressed the efficiency problem in neural networks. This segment addresses efficiencies in training, quantization, and optimizing the network and the hardware. Minimize the Bits (CNN Advanced Quantization) Training a CNN involves assigning weight vectors to certain results, and applying adaptive filters to those results to determine the positives, false positives, a... » read more

Speeding Up Neural Networks

Neural networking is gaining traction as the best way of collecting and moving critical data from the physical world and processing it in the digital world. Now the question is how to speed up this whole process. But it isn't a straightforward engineering challenge. Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. Th... » read more

Teaching Computers To See

Vision processing is emerging as a foundation technology for a number of high-growth applications, spurring a wave of intensive research to reduce power, improve performance, and push embedded vision into the mainstream to leverage economies of scale. What began as a relatively modest development effort has turned into an all-out race for a piece of this market, and for good reason. Mark... » read more