Computer Architecture analysis has a protracted historical past of creating simulators and instruments to judge and form the design of computer methods. For instance, the SimpleScalar simulator was launched within the late Nineties and allowed researchers to discover numerous microarchitectural concepts. Computer architecture simulators and instruments, equivalent to gem5, DRAMSys, and plenty of extra have performed a big position in advancing computer architecture 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 important advances within the area.
Nonetheless, computer architecture analysis is evolving, with business and academia turning in direction of machine learning (ML) optimization to satisfy stringent domain-specific necessities, equivalent to ML for computer architecture, ML for TinyML acceleration, DNN accelerator datapath optimization, reminiscence controllers, energy consumption, safety, and privateness. Although prior work has demonstrated the advantages of ML in design optimization, the dearth of robust, reproducible baselines hinders truthful and goal comparability throughout completely different strategies and poses a number of challenges to their deployment. To guarantee regular progress, it’s crucial to grasp and sort out these challenges collectively.
To alleviate these challenges, in “ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design”, accepted at ISCA 2023, we launched ArchGym, which incorporates quite a lot of computer architecture 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 architecture design parameters for every goal drawback; nobody answer 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 discovering the optimum architecture design, however selecting them is non-trivial. We launch the code and dataset throughout a number of computer architecture simulations and ML algorithms.
Challenges in ML-assisted architecture analysis
ML-assisted architecture analysis poses a number of challenges, together with:
- For a particular ML-assisted computer architecture drawback (e.g., discovering an optimum answer for a DRAM controller) there isn’t any systematic option to determine optimum ML algorithms or hyperparameters (e.g., learning price, warm-up steps, and many others.). There is a wider vary of ML and heuristic strategies, from random stroll to reinforcement learning (RL), that may be employed for design house exploration (DSE). While 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 architecture DSE, it’s essential to stipulate a scientific benchmarking methodology. - While computer architecture simulators have been the spine of architectural improvements, there’s an rising want to deal with the trade-offs between accuracy, pace, and price in architecture exploration. The accuracy and pace of efficiency estimation broadly varies from one simulator to a different, relying on the underlying modeling particulars (e.g., cycle-accurate vs. ML-based proxy fashions). While analytical or ML-based proxy fashions are nimble by advantage of discarding low-level particulars, they typically undergo from excessive prediction error. Also, resulting from business licensing, there might be strict limits on the variety of runs collected from a simulator. Overall, these constraints exhibit distinct efficiency vs. pattern effectivity trade-offs, affecting the selection of optimization algorithm for architecture exploration.
It is difficult to delineate methods to systematically examine the effectiveness of varied ML algorithms below these constraints. - Finally, the panorama of ML algorithms is quickly evolving and a few ML algorithms want knowledge to be helpful. Additionally, rendering the result of DSE into significant artifacts equivalent to datasets is important for drawing insights concerning the design house.
In this quickly evolving ecosystem, it’s consequential to make sure methods to amortize the overhead of search algorithms for architecture exploration. It shouldn’t be 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 completely different ML-based search algorithms pretty. It includes two essential parts: 1) the ArchGym atmosphere and a couple of) the ArchGym agent. The atmosphere is an encapsulation of the architecture price mannequin — which incorporates latency, throughput, space, vitality, and many others., to find out the computational price of operating 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, however, 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 essential indicators: {hardware} state, {hardware} parameters, and metrics. These indicators are the naked minimal to determine a significant communication channel between the atmosphere and the agent. Using these indicators, 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 perform of {hardware} efficiency metrics, equivalent to efficiency, vitality consumption, and many others.
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| ArchGym includes two essential 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 gives a unified framework for evaluating completely different ML-based search algorithms pretty whereas additionally saving the exploration knowledge because the ArchGym Dataset. |
ML algorithms could possibly be equally favorable to satisfy user-defined goal specs
Using ArchGym, we empirically reveal that throughout completely different optimization aims and DSE issues, a minimum of 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} explicit household of ML algorithms is best than one other. We present that with adequate hyperparameter tuning, completely different search algorithms, even random stroll (RW), are capable of determine the absolute best reward. However, notice that discovering the proper set of hyperparameters might require exhaustive search and even luck to make it aggressive.
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| With a adequate variety of samples, there exists a minimum of one set of hyperparameters that ends in the identical efficiency throughout a spread of search algorithms. Here the dashed line represents the utmost normalized reward. Cloud-1, cloud-2, stream, and random point out 4 completely different reminiscence traces for DRAMSys (DRAM subsystem design house exploration framework). |
Dataset development and high-fidelity proxy mannequin coaching
Creating a unified interface utilizing ArchGym additionally allows the creation of datasets that can be utilized to design higher data-driven ML-based proxy architecture price fashions to enhance the pace of architecture simulation. To consider the advantages of datasets in constructing an ML mannequin to approximate architecture price, we leverage ArchGym’s capability to log the information from every run from DRAMSys to create 4 dataset variants, every with a unique variety of knowledge factors. For every variant, we create two classes: (a) Diverse Dataset, which represents the information collected from completely different brokers (ACO, GA, RW, and BO), and (b) ACO solely, which reveals the information 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 improve the dataset dimension, the typical normalized root imply squared error (RMSE) barely decreases.
- However, as we introduce range within the dataset (e.g., amassing knowledge from completely different brokers), we observe 9× to 42× decrease RMSE throughout completely different dataset sizes.
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| Diverse dataset assortment throughout completely different brokers utilizing ArchGym interface. |
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| The affect of a various dataset and dataset dimension on the normalized RMSE. |
The want for a community-driven ecosystem for ML-assisted architecture analysis
While, ArchGym is an preliminary effort in direction of creating an open-source ecosystem that (1) connects a broad vary of search algorithms to computer architecture simulators in an unified and easy-to-extend method, (2) facilitates analysis in ML-assisted computer architecture, and (3) types the scaffold to develop reproducible baselines, there are numerous open challenges that want community-wide help. Below we define a number of the open challenges in ML-assisted architecture design. Addressing these challenges requires a effectively coordinated effort and a neighborhood pushed ecosystem.
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| Key challenges in ML-assisted architecture design. |
We name this ecosystem Architecture 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 computer architecture analysis. If you have an interest in serving to form this ecosystem, please fill out the curiosity survey.
Conclusion
ArchGym is an open supply gymnasium for ML architecture DSE and allows an standardized interface that may be readily prolonged to swimsuit completely different use instances. Additionally, ArchGym allows truthful and reproducible comparability between completely different ML algorithms and helps to determine stronger baselines for computer architecture analysis issues.
We invite the computer architecture neighborhood in addition to the ML neighborhood to actively take part within the growth of ArchGym. We consider that the creation of a gymnasium-type atmosphere for computer architecture analysis could be a big step ahead within the area 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 University. We want to acknowledge and spotlight Srivatsan Krishnan (Harvard) who contributed a number of concepts to this venture 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). In addition, we might additionally wish to thank James Laudon, Douglas Eck, Cliff Young, and Aleksandra Faust for their help, suggestions, and motivation for this work. We would additionally wish to thank John Guilyard for the animated determine used on this submit. Amir Yazdanbakhsh is now a Research Scientist at Google DeepMind and Vijay Janapa Reddi is an Associate Professor at Harvard.