Time
1 month
Location
USA
Sector
Hardware production
Description
The project’s primary objective was to validate a customer’s hypothesis concerning matrix multiplication through a specific process. The challenge lay in training language models on optical computers and simulating this procedure using Python.
Solution
To address this challenge, we developed three distinct applications:
- The first application served for testing classical machine learning models, particularly in the context of matrix multiplication.
- The second application was designed for seamless integration into any PyTorch-based solution, involving a fusion of PyTorch and C++ scripting.
- The third application focused on creating a simulation package optimized for GPUs, using BERT, a large-scale language model, as a proof of concept.
Results
The solution excelled in working effectively with classical machine learning models, demonstrating notable performance with the CIFAR dataset. However, when applied to large-scale language models, challenges surfaced. These challenges were thoroughly examined and documented, providing valuable insights for further development.
The client leveraged these insights to make data-driven decisions. This, in turn, attracted significant investments, propelling the client’s company into a multi-million-dollar business.