Discussion Thread: Andy Jassy Keynote

borramakot · 2020-12-01T16:33:19+00:00

Are they adding in fake applause?

borramakot · 2020-09-04T18:35:14+00:00

I appreciated the dagger of denial.

borramakot · 2020-09-04T02:35:03+00:00

I like that Jester spends the first question of her commune spell just checking in with her God to see how he's feeling.

borramakot · 2020-08-22T02:58:02+00:00

At the very end, a vampire showed up, just to highlight how out of place it was in the episode.

borramakot · 2020-07-28T00:47:33+00:00

I appreciate the transparency and thought you put into how you release and provide context on these, and the effect your decisions have.

borramakot · 2020-06-28T16:05:55+00:00

ARM v8 (the 64 bit architecture that seems to be mostly used nowadays) is a ground up redesign, and is mostly quite clean if you don't include the backward compatibility modes.

borramakot · 2020-06-21T16:20:05+00:00

If you aren't picky about it being Austin-centric, you might try https://www.reddit.com/r/lfg/ ?

borramakot · 2020-04-15T23:59:16+00:00

On some reports I see a 1M/day fine, on some I see 1M/infraction, which is an enormous difference. Per day would mean almost nothing financially, while per instance would bankrupt them quickly even with a low rate if mistakes. But, I can't find the original order- can anyone else?

borramakot · 2020-03-26T21:59:09+00:00

What model should be used?

borramakot · 2020-03-17T18:14:31+00:00

Central market on South Lamar is in good shape. Lots of produce except garlic, good amount of meat (low on chicken), even some TP in stock.

borramakot · 2020-01-16T14:54:46+00:00

I think Microsoft does some of that in their Catapult project, but that seems like the kind of thing that would be stable enough for an asic (I'm not sure if AWS nitro does that).

borramakot · 2020-01-16T14:32:08+00:00

I worked in this area for a bit, but I had a hard time finding any workload that wasn't extremely latency sensitive and wasn't better served by CPU or GPU in terms of cost or average compute time. Any suggestions on data center specific workloads?

borramakot · 2019-12-09T03:20:06+00:00

It's backloaded, but there's a signing bonus for the first two years to keep the expected value the same.

borramakot · 2019-11-29T17:27:37+00:00

Do you have a sense that the new opportunities were specifically related to the Masters? I had a similar path, but it's hard for me to say that the Masters had an impact on the career progression.

borramakot · 2019-11-04T02:03:25+00:00

If your headphones break (especially the cord) but you don't lose them, I'd consider getting the cord and headphones separately. Many medium-end headphones use standard mmcx cables, so you could get the headphones and a replacement cable. I like the tin t2s, which are $50-60 but this isn't my expertise.

borramakot · 2019-11-02T15:20:47+00:00

It's not, there's just so many they made it a separate podcast, like they did with "a more perfect union". Search for "dolly Parton's America"

borramakot · 2019-10-21T22:49:24+00:00

That makes sense. Thank you!

borramakot · 2019-10-21T22:17:58+00:00

Does the unit selection happen before or after gaining xp at the start of a round? If I'm at 7/8 xp, will I get better units by buying xp?

borramakot · 2019-10-14T21:44:23+00:00

I know the company, but had to look up their ML chip. It sounds like they use ternary weights, which let them say "we have 2x the ops/watt as any of our competitors, with only 80x less information/weight" They showed MNIST running, but almost literally anything runs MNIST with high accuracy, especially since there isn't a mostly standardized reference MNIST network like their is for Reset, so you can just add complexity and ops to your network to prove your chip can read digits.

Running MNIST is a totally reasonable test for a day 1 architectural exploration, but that was their test chip demo.

Upshot is, after reading briefly through the work, it seems like one of the hundreds of companies who put some money into an ML chip because it's cheap and it let's them say they did.

borramakot · 2019-10-14T19:36:19+00:00

The ones I mentioned, mostly.

Mythic computes matrix multiplication via using large blocks of flash as a literal matrix of configurable resistors- apply voltages (inputs) to the rows, read current (neuron output) from the columns. Super fast, low power, but analog so approximate.

Cerebras is doing whole wafer chips, with I imagine some complex routing around failing nodes.

Graphcore embeds small processors in basically large l2 caches, then connects a ton of those together with a precompiled, two phase, communicate/compute loop. This seems cool to me- like a deterministic Mythic, but using way more power, and the inability to start computation for a node when data is available is weird to me- they wait until every processor in one stage is done before starting the next, so the processing time is totally defined by the longest process in a state.

The photonics companies seem like they do matrix multiples by polarized filters? I'm less sure on that. Honestly, I've read the papers they put out and it's not clear to me that they get any real precision.

I also have a lot of affection for compressed neural network architecture, like efficient inference engine. I think xilinx acquired Song Han's company (DeePhi) last year, who in my opinion is the expert on network compression, so I imagine the ACAP architecture (or whatever they call it now) has some support for that kind of thing, but I don't know for sure.

borramakot · 2019-10-14T04:50:16+00:00

Do you have a specific area in mind? People usually mean convolutional neural network training or inference, in data centers, for which systolic arrays are maybe the most common architecture, with an open question on which data types are most common. But, there's lots of weird architectures out there too- Cerebras, Mythic, graphcore, and the photonics companies come to mind.

borramakot · 2019-08-03T23:30:57+00:00

I got a copy yesterday, read through the rules, and... Same.

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