Custom Accelerator

Custom accelerators are specialized hardware designed to significantly speed up the execution of computationally intensive machine learning models, primarily focusing on improving the performance of transformer networks and other deep learning architectures like PointNet. Current research emphasizes optimizing these accelerators for specific model components, such as the attention mechanism, and exploring diverse hardware platforms including FPGAs and RISC-V based many-core systems, often employing co-design approaches that integrate algorithm and hardware optimization. This work is crucial for deploying advanced AI models on resource-constrained devices and for achieving substantial performance gains in various applications, ranging from natural language processing and computer vision to recommendation systems.