We lifted 5 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ctuning/ck-tvm | pwc_unofficial | 0 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| ck_postprocess | Not yet run | ctuning/ck-tvm/program/image-classification-tvm/ck_postprocess.py code served (permissive licence) · get_code("a77cd04423243725") |
| get_top5 | Not yet run | ctuning/ck-tvm/program/image-classification-tvm/classify.py code served (permissive licence) · get_code("2a7ba538c8f051e5") |
| pre_path | Not yet run | ctuning/ck-tvm/package/lib-tvm-master-cpu/custom.py code served (permissive licence) · get_code("e2f22913f77585d2") |
| setup | Not yet run | ctuning/ck-tvm/package/lib-tvm-master-cpu/custom.py code served (permissive licence) · get_code("38053dd65bd84275") |
| transform_image | Not yet run | ctuning/ck-tvm/program/image-classification-tvm/classify.py code served (permissive licence) · get_code("e7267f02e6660d86") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms -- such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) -- requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1802.04799")
get_code_for_paper("1802.04799")
have("1802.04799")
Connect an agent — have() is free.