Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Design, Automation and Test in Europe Conference (DATE)
In the early design phase of a Deep Neural Network (DNN) acceleration system, fast energy and latency estimation are important to evaluate the optimality of different design candidates on algorithm, hardware, and algorithm-to-hardware mapping, given the gigantic design space. This work proposes a uniform intra-layer analytical latency model for DNN accelerators that can be used to evaluate diverse architectures and dataflows. It employs a 3-step approach to systematically estimate the latency breakdown of different system components, capture the operation state of each memory component, and identify stall-induced performance bottlenecks. To achieve high accuracy, different memory attributes, operands’ memory sharing scenarios, as well as dataflow implications have been taken into account. Validation against an in-house taped-out accelerator across various DNN layers has shown an average latency model accuracy of 94.3%. To showcase the capability of the proposed model, we carry out 3 case studies to assess respectively the impact of mapping, workloads, and diverse hardware architectures on latency, driving design insights for algorithm-hardware-mapping co-optimization.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon, Robert Künnemann