Machine learning uses GPUs, TPUs, FPGAs, and other ASICs to perform large numbers of calculations efficiently. The best hardware depends mainly on whether the priority is flexibility, rapid training, low-power inference, or maximum performance for a specific task.
Machine-learning models repeatedly perform operations on large arrays of data, particularly matrix and vector calculations. Hardware accelerators improve performance through parallel processing, meaning many calculations are executed simultaneously rather than sequentially.
| Hardware | Key characteristics | Typical machine-learning use |
|---|---|---|
| GPU (graphics processing unit) | Contains many smaller processing cores and is highly programmable. It can process many data elements concurrently. | Training and running neural networks, especially when flexibility and widely supported software are required. |
| TPU (tensor processing unit) | A specialized processor designed by Google for tensor and matrix operations. It offers high throughput for supported machine-learning workloads. | Training and inference using compatible frameworks, particularly in cloud-based systems. |
| FPGA (field-programmable gate array) | Its digital logic can be reconfigured after manufacture. It provides a balance between specialization and adaptability. | Low-latency inference, embedded systems, and applications where power consumption or hardware customization matters. |
| ASIC (application-specific integrated circuit) | Manufactured to perform a particular function. It can provide very high speed and energy efficiency but cannot be reprogrammed like an FPGA. | Large-scale or embedded inference where the algorithm and required operations are stable. |
A CPU (central processing unit) normally coordinates the system, prepares data, and handles general-purpose or sequential tasks. The accelerator performs the highly repetitive parallel calculations.
A common misconception is that TPUs and ASICs are completely separate categories. A TPU is itself a type of ASIC because it is designed for a specialized application; “ASIC” is the broader category.
For IB Computer Science A4.1, be prepared to compare these technologies using processing speed, parallelism, programmability, energy efficiency, cost, and suitability for training or inference. Do not merely expand each acronym: connect each hardware feature to a machine-learning requirement and justify which option best fits a stated scenario.
