Personal tools

Flexfloat DL Training Framework

From iis-projects

Revision as of 15:26, 19 November 2021 by Paulin (talk | contribs) (Created page with "thumb ==Short Description== So far, we have implemented inference of various smaller networks on our PULP-based systems ([pulp-nn]). The data-in...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
Manticore concept.png

Short Description

So far, we have implemented inference of various smaller networks on our PULP-based systems ([pulp-nn]). The data-intensive training of DNN was too memory-hungry to be implemented on our systems. Our latest architecture concept called Manticore includes 4096 snitch cores, is chiplet-based and includes HBM2 memory (see Image).


Recently, industry and academia have started exploring the required computational precision for training. Many state-of-the-art training hardware platforms support by now not only 64-bit and 32-bit floating-point formats, but also 16-bit floating-point formats (binary16 by IEEE and brainfloat). Recent work proposes various training formats such as 8-bit floats.


Status: Available

Looking for 1 Semester/Master student
Contact: :User:fischeti

Prerequisites

Machine Learning
Python
C


Prerequisites

  • Machine Learning
  • Python
  • C

Character

25% Theory
75% Implementation

Professor

Luca Benini

↑ top

Detailed Task Description

Goals

Practical Details

Results

Links

↑ top