LLM Acceleration

Accelerating Large Language Model Inference with Digital Circuit Design

The rapid growth of Large Language Models (LLMs) has led to an increasing demand for hardware with efficient and fast inference capabilities. One key challenge in achieving this is the limited memory bandwidth, which hinders the processing and creation of tokens, or fragments of words, that LLMs rely on.

Digital circuit design plays a crucial role in overcoming this bottleneck. By optimizing memory access patterns and developing novel architectures that can efficiently handle the massive amounts of data involved in LLM inference, custom digital circuit design can more than double memory bandwidth usage, significantly accelerating processing times.

This type of circuit design can be implemented on Field-Programmable Gate Arrays (FPGAs) much more quickly than in Application-Specific Integrated Circuits (ASICs). Harnessing performance, power efficiency, security, and scalability of semiconductor-based logic, they are very well-suited for LLM inference workloads.

Synogate leverages years of expertise in both digital circuit desing and profound understanding of machine learning to implement transformer architecture at transistor level to develop highly performant, adaptable and secure language processing systems.

Consumer GPU vs data center GPU vs custom chip