Deep Learning Neural Networks Drive Demands On Memory Bandwidth

A deep neural network (DNN) is a system that is designed similar to our current understanding of biological neural networks in the brain. DNNs are finding use in many applications, advancing at a fast pace, pushing the limits of existing silicon, and impacting the design of new computing architectures. Figure 1 shows a very basic form of neural network that has several nodes in each layer that ... » read more

Power/Performance Bits: March 27

Equalizing batteries Engineers at the University of Toledo propose a bilevel equalizer technology to improve the life span of batteries by combining the high performance of an active equalizer with the low cost of a passive equalizer. "Whenever we are talking about batteries, we are talking about cells connected in a series. Over time, the battery is not balanced and limited by the weakest ... » read more

Bridging Machine Learning’s Divide

There is a growing divide between those researching [getkc id="305" comment="machine learning"] (ML) in the cloud and those trying to perform inferencing using limited resources and power budgets. Researchers are using the most cost-effective hardware available to them, which happens to be GPUs filled with floating point arithmetic units. But this is an untenable solution for embedded infere... » read more