Hardware Approximation

Hardware approximation aims to reduce the resource consumption (power, area, latency) of computational tasks, particularly in machine learning, by accepting controlled accuracy losses. Current research focuses on developing approximation techniques across multiple layers of the hardware stack, from algorithmic modifications of models like MLPs and SVMs to circuit-level optimizations and novel architectures like time approximation for FIR filters and neural network-based LUTs for transformer inference. This approach is particularly relevant for resource-constrained applications such as printed electronics and battery-powered devices, enabling the deployment of complex models in previously inaccessible domains. The resulting energy efficiency gains and reduced hardware costs have significant implications for both the scalability of AI and the development of low-power, cost-effective computing systems.