Under way
Evaluation of Stochastic Computing for Efficient Deep Neural Network Inference
Type of work:
Master Thesis
Assignment:
- Literature review: Basic research about stochastic computing and hardware accelerators for machine learning.
- Development: Developing a software framework for basic computation of neural networks using stochastic computing.
- Integration: integrating the developed framework with Pytorch.
- Evaluation: Investigating the impact of using stochastic computing on the accuracy of neural network, throughput, and hardware implementation.
- Documentation: Writing thesis report consisting of framework implementation, setup, results and conclusion
Skills:
- Basic understanding of deep neural networks and stochastic computing
- C++/CUDA
- Pytorch
Background:
Deep neural networks (DNNs) require substantial memory and computational resources, posing challenges for deployment on battery-powered devices due to high energy consumption. Recent advancements have explored low bit-width data formats to simplify hardware implementation and reduce energy usage. However, accuracy loss with extremely low bit-widths, such as 1-bit, which enable basic operations implementation like multiplication with a single gate, remains a significant obstacle. Stochastic computing presents a promising alternative, allowing fundamental DNN operations like multiplication and accumulation to be performed with a single gate, leading to ultra-efficient computation. Despite its potential, this approach has not yet gained widespread attention and needs further investigation, especially for recent deep networks such as large language models.
Supervisor:
Student:
Javier Ferrer Ortega
Year:
2024