Revision as of 15:45, 15 February 2024

Overview

Status: Available

• Type: Master Thesis
• Professor: Prof. Dr. L. Benini
• Supervisors:
  • Luca Bertaccini: lbertaccini@iis.ee.ethz.ch

Introduction

FPnew block diagram [1]. Each operation group block can be instantiated through a parameter. In the figure, the FPU was instantiated without a DivSqrt module.

Low-precision floating-point (FP) formats are getting more and more traction in the context of neural network (NN) training. Employing low-precision formats, such as 8-bit FP data types, reduces the model's memory footprint and opens new opportunities to increase the system's energy efficiency. While many commercial platforms already provide support for 8-bit FP data types, introducing lower-than-8bit formats is key to facing the memory footprint and efficiency requirements that ever-larger NN models introduce.

FP unit (FPU) developed at IIS [1], [2] already provide hardware support for low-precision FP formats (down to 8 bits). The goal of this project is to explore less-than-8b FP formats with a particular emphasis on shared-exponent MiniFloats [3]. Such formats use a shared exponent for N less-than-8bit values and are currently being researched by many hardware providers [3].

Character

• 20% Literature / architecture review
• 40% RTL implementation
• 40% Evaluation

Prerequisites

• Strong interest in computer architecture
• Experience with digital design in SystemVerilog as taught in VLSI I
• Experience with ASIC implementation flow (synthesis) as taught in VLSI II

References

[1] https://arxiv.org/abs/2207.03192 MiniFloat-NN and ExSdotp: An ISA Extension and a Modular Open Hardware Unit for Low-Precision Training on RISC-V cores

[2] https://github.com/pulp-platform/cvfpu

[3] https://arxiv.org/abs/2310.10537