Laptop Structure analysis has an extended historical past of creating simulators and instruments to judge and form the design of pc programs. For instance, the SimpleScalar simulator was launched within the late Nineteen Nineties and allowed researchers to discover varied microarchitectural concepts. Laptop structure simulators and instruments, reminiscent of gem5, DRAMSys, and lots of extra have performed a big function in advancing pc structure analysis. Since then, these shared sources and infrastructure have benefited business and academia and have enabled researchers to systematically construct on one another’s work, resulting in vital advances within the subject.
Nonetheless, pc structure analysis is evolving, with business and academia turning in direction of machine studying (ML) optimization to satisfy stringent domain-specific necessities, reminiscent of ML for pc structure, ML for TinyML acceleration, DNN accelerator datapath optimization, reminiscence controllers, energy consumption, safety, and privateness. Though prior work has demonstrated the advantages of ML in design optimization, the dearth of robust, reproducible baselines hinders truthful and goal comparability throughout totally different strategies and poses a number of challenges to their deployment. To make sure regular progress, it’s crucial to know and sort out these challenges collectively.
To alleviate these challenges, in “ArchGym: An Open-Supply Gymnasium for Machine Studying Assisted Structure Design”, accepted at ISCA 2023, we launched ArchGym, which incorporates quite a lot of pc structure simulators and ML algorithms. Enabled by ArchGym, our outcomes point out that with a sufficiently massive variety of samples, any of a various assortment of ML algorithms are able to find the optimum set of structure design parameters for every goal downside; nobody resolution is essentially higher than one other. These outcomes additional point out that deciding on the optimum hyperparameters for a given ML algorithm is crucial for locating the optimum structure design, however selecting them is non-trivial. We launch the code and dataset throughout a number of pc structure simulations and ML algorithms.
Challenges in ML-assisted structure analysis
ML-assisted structure analysis poses a number of challenges, together with:
- For a particular ML-assisted pc structure downside (e.g., discovering an optimum resolution for a DRAM controller) there isn’t a systematic strategy to determine optimum ML algorithms or hyperparameters (e.g., studying price, warm-up steps, and many others.). There’s a wider vary of ML and heuristic strategies, from random stroll to reinforcement studying (RL), that may be employed for design area exploration (DSE). Whereas these strategies have proven noticeable efficiency enchancment over their selection of baselines, it’s not evident whether or not the enhancements are due to the selection of optimization algorithms or hyperparameters.
Thus, to make sure reproducibility and facilitate widespread adoption of ML-aided structure DSE, it’s crucial to stipulate a scientific benchmarking methodology. - Whereas pc structure simulators have been the spine of architectural improvements, there’s an rising want to handle the trade-offs between accuracy, velocity, and price in structure exploration. The accuracy and velocity of efficiency estimation extensively varies from one simulator to a different, relying on the underlying modeling particulars (e.g., cycle–correct vs. ML–based mostly proxy fashions). Whereas analytical or ML-based proxy fashions are nimble by advantage of discarding low-level particulars, they typically undergo from excessive prediction error. Additionally, resulting from industrial licensing, there could be strict limits on the variety of runs collected from a simulator. Total, these constraints exhibit distinct efficiency vs. pattern effectivity trade-offs, affecting the selection of optimization algorithm for structure exploration.
It’s difficult to delineate methods to systematically examine the effectiveness of varied ML algorithms underneath these constraints. - Lastly, the panorama of ML algorithms is quickly evolving and a few ML algorithms want knowledge to be helpful. Moreover, rendering the end result of DSE into significant artifacts reminiscent of datasets is essential for drawing insights concerning the design area.
On this quickly evolving ecosystem, it’s consequential to make sure methods to amortize the overhead of search algorithms for structure exploration. It’s not obvious, nor systematically studied methods to leverage exploration knowledge whereas being agnostic to the underlying search algorithm.
ArchGym design
ArchGym addresses these challenges by offering a unified framework for evaluating totally different ML-based search algorithms pretty. It includes two important parts: 1) the ArchGym atmosphere and a pair of) the ArchGym agent. The atmosphere is an encapsulation of the structure value mannequin — which incorporates latency, throughput, space, vitality, and many others., to find out the computational value of working the workload, given a set of architectural parameters — paired with the goal workload(s). The agent is an encapsulation of the ML algorithm used for the search and consists of hyperparameters and a guiding coverage. The hyperparameters are intrinsic to the algorithm for which the mannequin is to be optimized and might considerably affect efficiency. The coverage, then again, determines how the agent selects a parameter iteratively to optimize the goal goal.
Notably, ArchGym additionally features a standardized interface that connects these two parts, whereas additionally saving the exploration knowledge because the ArchGym Dataset. At its core, the interface entails three important alerts: {hardware} state, {hardware} parameters, and metrics. These alerts are the naked minimal to determine a significant communication channel between the atmosphere and the agent. Utilizing these alerts, the agent observes the state of the {hardware} and suggests a set of {hardware} parameters to iteratively optimize a (user-defined) reward. The reward is a operate of {hardware} efficiency metrics, reminiscent of efficiency, vitality consumption, and many others.
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| ArchGym includes two important parts: the ArchGym atmosphere and the ArchGym agent. The ArchGym atmosphere encapsulates the price mannequin and the agent is an abstraction of a coverage and hyperparameters. With a standardized interface that connects these two parts, ArchGym offers a unified framework for evaluating totally different ML-based search algorithms pretty whereas additionally saving the exploration knowledge because the ArchGym Dataset. |
ML algorithms might be equally favorable to satisfy user-defined goal specs
Utilizing ArchGym, we empirically reveal that throughout totally different optimization goals and DSE issues, at the very least one set of hyperparameters exists that ends in the identical {hardware} efficiency as different ML algorithms. A poorly chosen (random choice) hyperparameter for the ML algorithm or its baseline can result in a deceptive conclusion {that a} specific household of ML algorithms is best than one other. We present that with ample hyperparameter tuning, totally different search algorithms, even random stroll (RW), are in a position to determine the very best reward. Nonetheless, notice that discovering the appropriate set of hyperparameters could require exhaustive search and even luck to make it aggressive.
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| With a ample variety of samples, there exists at the very least one set of hyperparameters that ends in the identical efficiency throughout a spread of search algorithms. Right here the dashed line represents the utmost normalized reward. Cloud-1, cloud-2, stream, and random point out 4 totally different reminiscence traces for DRAMSys (DRAM subsystem design area exploration framework). |
Dataset building and high-fidelity proxy mannequin coaching
Making a unified interface utilizing ArchGym additionally allows the creation of datasets that can be utilized to design higher data-driven ML-based proxy structure value fashions to enhance the velocity of structure simulation. To judge the advantages of datasets in constructing an ML mannequin to approximate structure value, we leverage ArchGym’s capacity to log the info from every run from DRAMSys to create 4 dataset variants, every with a special variety of knowledge factors. For every variant, we create two classes: (a) Numerous Dataset, which represents the info collected from totally different brokers (ACO, GA, RW, and BO), and (b) ACO solely, which exhibits the info collected completely from the ACO agent, each of that are launched together with ArchGym. We practice a proxy mannequin on every dataset utilizing random forest regression with the target to foretell the latency of designs for a DRAM simulator. Our outcomes present that:
- As we enhance the dataset dimension, the typical normalized root imply squared error (RMSE) barely decreases.
- Nonetheless, as we introduce range within the dataset (e.g., amassing knowledge from totally different brokers), we observe 9× to 42× decrease RMSE throughout totally different dataset sizes.
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| Numerous dataset assortment throughout totally different brokers utilizing ArchGym interface. |
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| The affect of a various dataset and dataset dimension on the normalized RMSE. |
The necessity for a community-driven ecosystem for ML-assisted structure analysis
Whereas, ArchGym is an preliminary effort in direction of creating an open-source ecosystem that (1) connects a broad vary of search algorithms to pc structure simulators in an unified and easy-to-extend method, (2) facilitates analysis in ML-assisted pc structure, and (3) types the scaffold to develop reproducible baselines, there are numerous open challenges that want community-wide help. Under we define a number of the open challenges in ML-assisted structure design. Addressing these challenges requires a effectively coordinated effort and a group pushed ecosystem.
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| Key challenges in ML-assisted structure design. |
We name this ecosystem Structure 2.0. We define the important thing challenges and a imaginative and prescient for constructing an inclusive ecosystem of interdisciplinary researchers to sort out the long-standing open issues in making use of ML for pc structure analysis. If you’re concerned about serving to form this ecosystem, please fill out the curiosity survey.
Conclusion
ArchGym is an open supply gymnasium for ML structure DSE and allows an standardized interface that may be readily prolonged to swimsuit totally different use circumstances. Moreover, ArchGym allows truthful and reproducible comparability between totally different ML algorithms and helps to determine stronger baselines for pc structure analysis issues.
We invite the pc structure group in addition to the ML group to actively take part within the improvement of ArchGym. We imagine that the creation of a gymnasium-type atmosphere for pc structure analysis could be a big step ahead within the subject and supply a platform for researchers to make use of ML to speed up analysis and result in new and modern designs.
Acknowledgements
This blogpost is predicated on joint work with a number of co-authors at Google and Harvard College. We wish to acknowledge and spotlight Srivatsan Krishnan (Harvard) who contributed a number of concepts to this mission in collaboration with Shvetank Prakash (Harvard), Jason Jabbour (Harvard), Ikechukwu Uchendu (Harvard), Susobhan Ghosh (Harvard), Behzad Boroujerdian (Harvard), Daniel Richins (Harvard), Devashree Tripathy (Harvard), and Thierry Thambe (Harvard). As well as, we might additionally wish to thank James Laudon, Douglas Eck, Cliff Younger, and Aleksandra Faust for his or her help, suggestions, and motivation for this work. We might additionally wish to thank John Guilyard for the animated determine used on this publish. Amir Yazdanbakhsh is now a Analysis Scientist at Google DeepMind and Vijay Janapa Reddi is an Affiliate Professor at Harvard.
