VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation

no code implementations • 6 Jun 2024 • Prashanth Vijayaraghavan, Luyao Shi, Stefano Ambrogio, Charles Mackin, Apoorva Nitsure, David Beymer, Ehsan Degan

With the unprecedented advancements in Large Language Models (LLMs), their application domains have expanded to include code generation tasks across various programming languages.

Code Generation In-Context Learning +1

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

1 code implementation • 18 Jul 2023 • Manuel Le Gallo, Corey Lammie, Julian Buechel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch

In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github. com/IBM/aihwkit.

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

no code implementations • 16 Feb 2023 • Malte J. Rasch, Charles Mackin, Manuel Le Gallo, An Chen, Andrea Fasoli, Frederic Odermatt, Ning li, S. R. Nandakumar, Pritish Narayanan, Hsinyu Tsai, Geoffrey W. Burr, Abu Sebastian, Vijay Narayanan

Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear.