The recent explosion of artificial intelligence capabilities relies heavily on specialised hardware accelerators. Advanced AI algorithms have huge computational demands, driving intense innovation in custom processors tailored for machine learning workloads. In this article, we dive deeper into the distinct architectures of GPUs, ASICs, FPGAs, and neuromorphic chips powering AI's rapid rise.

GPU Architectures for Massively Parallel Processing

Modern Graphical Processing Units (GPUs) contain arrays of hundreds to thousands of small, efficient cores to enable massively parallel processing. This makes them well-suited for AI workloads. Matrix maths operations key to deep learning, like matrix multiplication, can be distributed across many cores executing simultaneously.

For example, Nvidia's Tensor Cores speed up matrix calculus by processing parts across 64 or 128 FP16 or bfloat16 units at once. The results are then accumulated via a tree adder for high throughput. Further, GPUs utilise fast shared memory and caches to reuse data locally, avoiding slow trips to main DRAM.

However, GPUs do have high power demands, on the order of 300W for top models. Their general-purpose 32-bit floating point units are also overkill for AI's low-precision maths. Still, through immense parallelism and memory optimisations, today's GPUs deliver teraflops of performance for training deep neural networks.