Bosch Deep Learning Hardware Benchmark
This work addresses the problem of efficient hardware comparison for embedded deep learning inference in autonomous driving, but it is incremental as it builds on existing benchmarks with specific enhancements.
The authors tackled the challenge of comparing hardware accelerators for deep learning inference by developing a benchmark specifically for embedded systems and autonomous driving tasks, introducing a new granularity level for evaluating submodules, a twofold procedure for hardware and model optimizations, and an extended set of performance indicators.
The widespread use of Deep Learning (DL) applications in science and industry has created a large demand for efficient inference systems. This has resulted in a rapid increase of available Hardware Accelerators (HWAs) making comparison challenging and laborious. To address this, several DL hardware benchmarks have been proposed aiming at a comprehensive comparison for many models, tasks, and hardware platforms. Here, we present our DL hardware benchmark which has been specifically developed for inference on embedded HWAs and tasks required for autonomous driving. In addition to previous benchmarks, we propose a new granularity level to evaluate common submodules of DL models, a twofold benchmark procedure that accounts for hardware and model optimizations done by HWA manufacturers, and an extended set of performance indicators that can help to identify a mismatch between a HWA and the DL models used in our benchmark.