Instead of relying solely on the CPU to do all the heavy lifting, specific tasks are offloaded to these accelerators. For example, GPUs are particularly good at handling parallel operations, which makes them ideal for graphics rendering, machine learning, and scientific simulations. TPUs, which were originally developed by Google, are optimized for neural network workloads, while FPGAs can be customized for more specific computing tasks like financial modeling or video encoding.
Why accelerated computing matters
Traditional CPUs like the one you have in your living room or office are powerful, but they are also very general. Workloads have become more specialized, like LLM training or simulating protein folding for drug discovery. The energy consumption needed for AI is infinite, so the need for tailored processing power has grown exponentially.
Many of the world’s most important breakthroughs in AI, healthcare, climate modeling, and autonomous vehicles rely on accelerated computing. It’s what allows self-driving cars to interpret their surroundings in milliseconds and helps researchers run simulations that would take weeks on conventional systems. The impact is especially noticeable in terms of energy efficiency. Accelerated systems can perform tasks using less power because they’re optimized for the job. For businesses running massive data centers, this can be a huge boon to increasing energy efficiency and driving down consumption costs.