DNN Accelerator
DNN accelerators are specialized hardware designed to efficiently execute deep neural network (DNN) computations, primarily aiming to improve speed, reduce energy consumption, and minimize latency. Current research focuses on optimizing various aspects of these accelerators, including novel memory hierarchies, efficient in-memory computing (IMC) using stochastic processing, and adaptive hardware/software co-optimization techniques, often applied to models like ResNet and Vision Transformers. These advancements are crucial for deploying DNNs on resource-constrained edge devices and in safety-critical applications, impacting both the efficiency of AI systems and their reliability in real-world deployments.
Papers
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
Sheng-Chun Kao, Michael Pellauer, Angshuman Parashar, Tushar Krishna
DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators
Sheng-Chun Kao, Xiaoyu Huang, Tushar Krishna