A Machine Learning Accelerator is specialised hardware designed to execute the dense linear-algebra workloads of neural networks far more efficiently than general-purpose CPUs. It optimises matrix multiplication, convolution and tensor operations through massive parallelism, dedicated multiply-accumulate arrays and high memory bandwidth. Examples span GPUs, tensor processing units, FPGAs and custom ASICs deployed from data centres to edge and embedded devices.
Overview
- ML accelerators address the gap between the explosive compute demands of deep learning and the limited throughput of scalar CPUs. By embedding thousands of parallel multiply-accumulate units close to fast memory, they sustain the tensor operations that dominate training and inference. Architectures range from flexible GPUs to fixed-function ASICs, trading programmability for energy efficiency.
Mechanisms
- Systolic and SIMD arrays: dense grids of multiply-accumulate units executing matrix operations.
- Memory hierarchy: high-bandwidth memory and on-chip buffers minimising data movement.
- Reduced precision: bfloat16, int8 and other formats trading accuracy for throughput and energy.
- Dataflow optimisation: scheduling computation to maximise reuse and minimise off-chip traffic.
- Deployment tiers: data-centre training accelerators versus low-power edge inference chips.
Applications
- Training large neural networks in data centres.
- Low-latency inference for vision and language models.
- On-device intelligence in spatial-computing headsets and mobile devices.
- Edge accelerators for robotics, autonomous systems and IoT.