Application-specific hardware accelerators have become a new efficiency-boosting paradigm in the machine learning research community and in industry. Designing and optimizing these hardware accelerators however requires considerable manual effort as well as time- and energy-consuming simulations. Moreover, such simulation-driven approaches must be re-run from scratch whenever the set of target applications or design constraints change.
To reduce the costs of simulation-driven approaches, a team from Google Research and UC Berkeley has proposed PRIME, an offline data-driven method that utilizes logged simulation data to automatically architect hardware accelerators without the use of simulations. Compared to state-of-the-art simulation-driven methods on single and multiple applications, PRIME achieves impressive 1.54× and 1.20× performance improvements, while significantly reducing the required total simulation time by 93 percent and 99 percent, respectively.
The team summarises the benefits of their data-driven approach to hardware accelerator design as:
- It significantly shortens the recurring cost of running large-scale simulation sweeps.
- It alleviates the need to explicitly bake-in domain knowledge or search space pruning.
- It enables data re-use by empowering the designer to optimize accelerators for new unseen applications, by the virtue of effective generalization.
PRIME is a data-driven approach that can automatically architect high-performing application-specific accelerators. It does this using only previously collected offline data, learning a robust surrogate model of the task objective function from an existing offline dataset. PRIME can also be used for multi-model and zero-shot optimization, capabilities lacking in existing approaches.
The researchers compared PRIME with three state-of-the-art simulator-driven methods: 1) evolutionary search with the firefly optimizer; 2) Bayesian optimization implemented via the Google Vizier framework; and 3) a state-of-the-art online MBO method for designing biological sequences.
In empirical evaluations, PRIME outperformed the best designs observed in the logged data by 2.46× and improved on the best simulator-driven approach by about 1.54×. In the more challenging zero-shot learning setting, PRIME outperformed simulator-driven methods by 1.2× while reducing the total simulation time by 99 percent.
Overall, PRIME’s performance demonstrates the promise and potential of utilizing logged offline data and strong offline methods in a hardware acceleration design pipeline. The researchers suggest future studies could leverage PRIME for other problems in architecture and systems, such as software-hardware co-optimization.
The paper Data-Driven Offline Optimization for Architecting Hardware Accelerators is on arXiv.
Author: Hecate He | Editor: Michael Sarazen
We know you don’t want to miss any news or research breakthroughs. Subscribe to our popular newsletter Synced Global AI Weekly to get weekly AI updates.
Pingback: Google & UC Berkeley's Data-Driven Offline Optimization Approach Significantly Boosts Hardware Accelerator Performance, Reduces Simulation Time by More Than 90% – Synced - AI Caosuo
In fact, now data-driven marketing is very important for every business, and should also help to better build all strategies, and therefore achieve results. The easiest thing you can do is hire the right people