TVM性能评估分析(三) Figure 1. TVM's WebGPU backend close to native GPU performance when deploying models to the web. Figure 2. WebGPU is to write shaders for primitive operators in deep neural networks Figure 3. Build a WebGPU runtime inside TVM's JS runt…
TVM性能评估分析(七) Figure 1. Performance Improvement Figure 2. Depthwise convolution Figure 3. Data Fusion Figure 4. Data Fusion(2) Figure 5. Shared memory can be seen as cache in GPU. It is on-chip and much faster than global memory. Figure 6. Shar…
TVM性能评估分析(六) Figure 1. The workflow of development PC, compile, deploy to the device, test, then modify the codes again to see whether it accelerates. Figure 2. The Android APP takes shared library as input and runs compiled functions on the mobil…
TVM性能评估分析(五) Figure 3. A futher speed up with operator fusion Table 1. Performance issue of cuBLAS' batch matmul Table 2. Finding the best combination of number_thread. The results are obtained on a NVIDIA M40 GPU device with CUDA8.0. Figure 4. D…
TVM性能评估分析(四) Figure 1. Efficient Privacy-Preserving ML Using TVM Figure 2. Motivation: Privacy-Preserving ML Figure 3. Backend Figure 4. Differential privacy (DP) provides a formal guarantee that models trained on similar datasets are indistinguis…
TVM性能评估分析(二) Figure 1. A bird's eye view of the µTVM + AutoTVM infrastructure Figure 2. A standard µTVM setup, where the host communicates with the device via JTAG. Figure 3. The performance results of MicroTVM Figure 4. Improved performance by ~2…
TVM性能评估分析(一) System Overview AutoTVM vs Auto-scheduler Table 1. Workflow Comparision Figure 1. Search Process Overview Figure 2. Code Performance Comparision (Higher is better) Figure 3. Search Time Comparision (Lower is better) Figure 4. The expecte…
