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Sam Goto samuelgoto

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Discovery

<link rel="alternate" href="/api" type="application/vnd.microforms">

Self-described payloads

{

JSON-LD encapsulation of forms:

{
  @context: "https://example.com",
  @type: MyType,
  action: {
    @context: "https://w3c.org/2018/forms",
    @type: Form,
 ...

CoreML/ONNX/TF JSON-LD based for serving.

{
  @context: "https://w3c.org/2018/deeplearning",
  @type: Model,
  network: {
    @type: NeuralNetwork,
    ...
 }

ATOM feeds in JSON-LD.

{
  "@context": "http://www.w3.org/2005/Atom",
  "@type": "Feed",
  "title": "Example Feed",
  "subtitle": "A subtitle.",
  "links": [{

Alternatives considered

TODO(goto): go over this.

ARML is an XML-based data format to describe an interact with AR scenes.

ARML

Deep Learning Processing Units

10-15% cost of interpreation seems like a non-starter to me when they are trying to squeeze in every level of performance. I'm seeing some convergence in the industry regarding "inference models" file formats / representations

I would challenge that assertion. Your going to get a larger drop on an android phone when thermal throttling kicks in.

Prior Art

  • web assembly will always have a 10-12% interpretation cost (anecdotal data from @bnelson, need to sanity check / validate with others), which for computer vision things over a came stream seems like a non starter.
  • composition, portability
  • ergonomics (don't have to care about gpu)
  • cpu vs gpu: memory constrained versus computing contrained
  • concurrency, multithreading: don't you need to transfer video streams back and fourth between worker threads?

Things that could be interesting to control/censor:

Things that "use strict" control: