Tvm Dl Compiler Eval

---
name: tvm-dl-compiler-eval
description: Evaluates the end-to-end performance and optimization capability of a deep learning compiler across diverse hardware back-ends (GPU, CPU, embedded GPU, FPGA) on standard inference workloads. It measures how effectively the compiler automatically generates high-performance kernels compared to hand-tuned vendor libraries and existing frameworks. Use when the user wants to benchmark on DL Inference Workloads (ResNet-18, MobileNet, LSTM, DQN, DCGAN), or asks about evaluating this task. Reports speedup.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1802.04799
  bibtex_key: chen2018tvm
  confidence: high
---

# tvm-dl-compiler-eval

> TVM: An Automated End-to-End Optimizing Compiler for Deep Learning — Chen et al. (2018) (arXiv:1802.04799, 2018)

## What this evaluates

Evaluates the end-to-end performance and optimization capability of a deep learning compiler across diverse hardware back-ends (GPU, CPU, embedded GPU, FPGA) on standard inference workloads. It measures how effectively the compiler automatically generates high-performance kernels compared to hand-tuned vendor libraries and existing frameworks.

## Datasets

- **DL Inference Workloads (ResNet-18, MobileNet, LSTM, DQN, DCGAN)** — total ?; splits: (unstated)

## Metrics

- `speedup` **(primary)** — range: other
  - Calculated as the execution time of the baseline framework/library divided by the execution time of the TVM-generated kernel for the same workload and hardware configuration.

## Input / output format

**Input**: Deep learning computational graphs with specified operator configurations (e.g., conv2d dimensions, kernel size, stride, padding) and target hardware backend specifications.

**Output**: Hardware-specific optimized kernel code (e.g., CUDA, C, ARM assembly) and runtime execution binaries capable of running on the target platform.

## Scoring recipe

```python
def compute_speedup(baseline_time, tvm_time):
    if tvm_time <= 0:
        return float('inf')
    return baseline_time / tvm_time

# Evaluated per workload and hardware backend.
# Baselines include MXNet, TensorFlow, TFLite, ARM Compute Library, Caffe2.
```

## Common pitfalls

- Comparing TVM against baselines that use heavily hand-tuned vendor libraries (e.g., cuDNN, cuBLAS) which may have operator coverage advantages not available to auto-generated compilers.
- Ignoring workload/operator support gaps in baselines (e.g., depthwise conv, low-precision ops, LSTM, DCGAN were unsupported by some baselines at the time).
- Overlooking Amdahl's law bottlenecks on hybrid systems (e.g., FPGA offloading speedup is limited by CPU-bound residual/activation layers).

## Evidence (verbatim from paper)

> TVM outperforms the baselines, with speedups ranging from 1.6× to 3.8× due to both joint graph optimization and the automatic optimizer, which generates high-performance fused operators.

## Citation

```bibtex
@misc{chen2018tvm,
  title={TVM: An Automated End-to-End Optimizing Compiler for Deep Learning},
  author={Chen et al. (2018)},
  year={2018},
  note={arXiv:1802.04799}
}
```

- arXiv: 1802.04799