Advanced hardware acceleration techniques
Advanced hardware acceleration techniques are pivotal in enhancing the capabilities of LLMs, by significantly boosting the speed and efficiency of necessary computations for their training and inference phases. Beyond the primary use of GPUs, TPUs, and FPGAs, let’s explore some more sophisticated aspects and emerging trends in hardware acceleration that are pushing the boundaries of what’s possible with LLMs.
Tensor cores
Tensor cores are a breakthrough in GPU architecture, designed to accelerate the matrix multiplications that power deep learning workloads. They enable mixed-precision arithmetic, a technique that uses different numerical precisions within the same computation. Here’s how they contribute to deep learning:
- Efficient matrix operations: Tensor cores are optimized to perform the matrix multiplication and accumulation operations at the heart of neural network training and inference. They can carry...
