Efficient Hardware

Efficient hardware design for deep learning and other computationally intensive tasks focuses on minimizing energy consumption and latency while maximizing throughput. Current research emphasizes developing hardware-aware model architectures (like LowFormer and EfficientRep) and algorithms optimized for specific hardware platforms (e.g., FPGAs, specialized ASICs), often incorporating techniques like sparsity, quantization, and novel numerical formats. These advancements are crucial for deploying complex models on resource-constrained devices, enabling broader applications in areas such as mobile computing, edge AI, and high-performance computing, and driving progress in energy-efficient computing.