Industrial

[From the last episode: We looked at what it means to do machine learning “at the edge,” and some of the compromises that must be made.] When doing ML at the edge, we want two things: less computing (for speed and, especially, for energy) and smaller hardware that requires less […]

[From the last episode: We looked at activation and what they’re for.] We’ve talked about the structure of machine-learning (ML) models and much of the hardware and math needed to do ML work. But there are some practical considerations that mean we may not directly use the pristine model as […]

[From the last episode: We looked at the notion of sparsity and how it helps with the math.] We saw before that there are three main elements in a CNN: the convolution, the pooling, and the activation . Today we focus on activation . I’ll start by saying that the […]

[From the last episode: We looked at the reason for doing all the multiply-accumulate math with machine learning.] Before we leave this matrix multiplication thing, there’s one other notion floating about a lot these days. Anyone listening to conversations about how to make machine-learning (ML) algorithms faster will eventually hear […]

[From the last episode: We looked at the reasons for all of the multiply-accumulate hardware in machine-learning engines.] OK, we’ve spent a couple of weeks deep in the math used for machine learning (ML). Now let’s back up a second to look at what this all means in the bigger […]