| Automatic generation of high-performance quantized machine learning kernels. | 1 | 0.34 | 2020 |
| Relay: A High-Level IR for Deep Learning. | 0 | 0.34 | 2019 |
| A Hardware–Software Blueprint for Flexible Deep Learning Specialization | 8 | 0.57 | 2019 |
| Exploiting Errors for Efficiency: A Survey from Circuits to Applications | 2 | 0.36 | 2018 |
| A Taxonomy of General Purpose Approximate Computing Techniques. | 5 | 0.39 | 2018 |
| Automating Generation of Low Precision Deep Learning Operators. | 0 | 0.34 | 2018 |
| VTA: An Open Hardware-Software Stack for Deep Learning. | 1 | 0.35 | 2018 |
| Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference. | 0 | 0.34 | 2018 |
| MATIC: Learning around errors for efficient low-voltage neural network accelerators | 6 | 0.44 | 2018 |
| TVM: End-to-End Optimization Stack for Deep Learning. | 12 | 0.68 | 2018 |
| PANEL: Open panel and discussion on tackling complexity, reproducibility and tech transfer challenges in a rapidly evolving AI/ML/systems research | 0 | 0.34 | 2018 |
| Learning to Optimize Tensor Programs. | 1 | 0.35 | 2018 |
| Energy-Efficient Neural Network Acceleration in the Presence of Bit-Level Memory Errors. | 8 | 0.54 | 2018 |
| Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments. | 0 | 0.34 | 2018 |
| TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. | 11 | 0.59 | 2018 |
| Exploring computation-communication tradeoffs in camera systems | 5 | 0.45 | 2017 |
| SNNAP: Approximate computing on programmable SoCs via neural acceleration | 40 | 1.33 | 2015 |
| Approximate Computing: Making Mobile Systems More Efficient | 5 | 0.46 | 2015 |
