DORA: Dataflow-Instruction Orchestration Architecture for DNN Acceleration
Abstract
As deep neural networks develop significantly more diverse and complex, achieving high performance and efficiency on complicated DNN models faces pressing challenges. Modern DNN workloads are increasingly diverse in operation types, tensor shapes, and execution dependencies, making it difficult to sustain high hardware efficiency across models. In addition, a generic accelerator often incurs substantial overhead when executing diverse workloads.
Fields of Study & Subjects
Advanced Neural Network Applications
Adversarial Robustness in Machine Learning
Parallel Computing and Optimization Techniques
Publication details
- Journal / source
- Proceedings of the Great Lakes Symposium on VLSI 2026
- Publisher
- ACM
- Pages
- 487–494
- Publication date
- 18 June 2026