Given my last post was in November of 2013 (trust me, I’ve been busy), I figured I’d start out with a heady topic like “Neural
Networking” in an age where Deep Machine Learning and perhaps its lesser cousin, assisted Machine Learning (I’ll define in a bit), seem to be all the rage. However, before we begin, I want to make a few things clear:
I’m no expert in these fields.
I’m musing out loud here. You’re my audience and what you determine to be salient and what you deem junk is, well, your problem, not mine.
DML/AML, Neural Networking, and a whole host of other terms, acronyms, mindf**k level events, etc. are here. Deal with it.
So with such an illustrious preface, I suppose we should let the party begin.
So, a big deal was made about a paper from some university researchers somewhere (source here) noting the efficacy of using GPGPUs for Network Analysis (obstensibly as a
correlation sieve for all the streams of information being presented). While this is a GREAT paper, it neglects a few critical points that someone who’s a bit more familiar with the platforms might pick up.
the rise of APUs.
Power consumption and Thermals
Let’s tackle these individually (and yes, in a cursory manner):
In previous Cloud Optimized Storage Solution articles, I’ve discussed the content being stored, the method of storage, as well as principles derived from data tiering. Today, I want to jump ahead a bit and discuss how neural networks and heuristics can impact the processing of object and file data for the cloud.
One of the more […]
In developing the Future Storage System series, I have been trying to take a part of my excitement for storage technologies and overlay them with systems/platform technology. Typically, the storage industry lags on the platform development side of the house (mostly out of necessity). So, part of looking at the Future Storage System was to […]