With the fast evolvement of neural network models, existing solutions lack the support for more complicated dataflow and operations, including reduction operation, sparse operation and nonlinear functions, etc. Properly leveraging the reconfigurability of the hardware, we are able to address those challenges while not introducing programming overhead. RAVEN was among the finalists of Qualcomm Innovation Fellowship in 2019. Currently, RAVEN is still in progress.
- RAVEN focuses on the time-scalability of DL models
- SECO: A Scalable Accuracy Approximate Exponential Function Via Cross-Layer Optimization
- ISCA'20 paper is selected in IEEE Micro Top Picks 2021
- Approximate Hardware Techniques for Energy-Quality Scaling Across the System
- Deep Learning and Stochastic Computing