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Mapping Networks on Reconfigurable Binary Engine Accelerator

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Short Description

We have recently designed an accelerator called Reconfigurable Binary Engine (RBE). The RBE architecture uses these two innovations to emulate quantized NNs by choosing the binary weights to correspond to each bit of the quantized weights. One quantized NN can therefore be emulated by a superposition of power-of-2 weighted Q) × Q+ binary NN, whereas Q+ corresponds to the quantization level of the weights and Q) quantization level of the activations. We call this concept from now on Binary Based Quantization (BBQ) which allows the RBE to perform convolutions with configurable arithmetic precisions in a flexible and power-scalable way. In this project we make use of our in-house developed frameworks NEMO and DORY to map networks onto the RBE accelerator and evaluate its performance and energy-efficiency for real networks.


Status: Available

Looking for 1-2 Semester/Master students
Contact: Gianna Paulin Thorir Mar Ingolfsson

Prerequisites

  • VLSI I
  • C coding
  • python coding (optimal: Pytorch)

Character

20% Theory
20% HW understanding
40% ML Tools: Nemo, Dory, Pytorch
20% Embedded C programming

Professor

Luca Benini

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