An open-source gymnasium for machine studying assisted laptop structure design – Google Analysis Weblog


Laptop Structure analysis has an extended historical past of growing simulators and instruments to guage and form the design of laptop 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, similar to gem5, DRAMSys, and lots of extra have performed a big function in advancing laptop structure analysis. Since then, these shared assets and infrastructure have benefited trade and academia and have enabled researchers to systematically construct on one another’s work, resulting in important advances within the discipline.

Nonetheless, laptop structure analysis is evolving, with trade and academia turning in the direction of machine studying (ML) optimization to fulfill stringent domain-specific necessities, similar to ML for laptop structure, ML for TinyML accelerationDNN 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 sturdy, reproducible baselines hinders truthful and goal comparability throughout completely different strategies and poses a number of challenges to their deployment. To make sure regular progress, it’s crucial to grasp 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 laptop structure simulators and ML algorithms. Enabled by ArchGym, our outcomes point out that with a sufficiently giant 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 choosing 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 laptop structure simulations and ML algorithms.

Challenges in ML-assisted structure analysis

ML-assisted structure analysis poses a number of challenges, together with:

  1. For a particular ML-assisted laptop structure downside (e.g., discovering an optimum resolution for a DRAM controller) there isn’t any systematic technique to establish 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 house exploration (DSE). Whereas these strategies have proven noticeable efficiency enchancment over their alternative 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 obligatory to stipulate a scientific benchmarking methodology.
  2. Whereas laptop structure simulators have been the spine of architectural improvements, there may be an rising want to deal with 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., cyclecorrect vs. MLbased 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, as a consequence of industrial licensing, there could be strict limits on the variety of runs collected from a simulator. General, these constraints exhibit distinct efficiency vs. pattern effectivity trade-offs, affecting the selection of optimization algorithm for structure exploration.
    It’s difficult to delineate tips on how to systematically examine the effectiveness of varied ML algorithms underneath these constraints.
  3. Lastly, the panorama of ML algorithms is quickly evolving and a few ML algorithms want knowledge to be helpful. Moreover, rendering the result of DSE into significant artifacts similar to datasets is crucial for drawing insights concerning the design house.
    On this quickly evolving ecosystem, it’s consequential to make sure tips on how to amortize the overhead of search algorithms for structure exploration. It isn’t obvious, nor systematically studied tips on how 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 predominant elements: 1) the ArchGym setting and a couple of) the ArchGym agent. The setting is an encapsulation of the structure price mannequin — which incorporates latency, throughput, space, vitality, and many others., to find out the computational price 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 may considerably affect efficiency. The coverage, alternatively, determines how the agent selects a parameter iteratively to optimize the goal goal.

Notably, ArchGym additionally features a standardized interface that connects these two elements, whereas additionally saving the exploration knowledge because the ArchGym Dataset. At its core, the interface entails three predominant alerts: {hardware} state, {hardware} parameters, and metrics. These alerts are the naked minimal to determine a significant communication channel between the setting 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, similar to efficiency, vitality consumption, and many others. 

ArchGym includes two predominant elements: the ArchGym setting and the ArchGym agent. The ArchGym setting encapsulates the price mannequin and the agent is an abstraction of a coverage and hyperparameters. With a standardized interface that connects these two elements, ArchGym offers a unified framework for evaluating completely different ML-based search algorithms pretty whereas additionally saving the exploration knowledge because the ArchGym Dataset.

ML algorithms might be equally favorable to fulfill user-defined goal specs

Utilizing ArchGym, we empirically exhibit that throughout completely different optimization targets and DSE issues, a minimum of one set of hyperparameters exists that leads to 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 in a position to establish the very best reward. Nevertheless, word that discovering the suitable set of hyperparameters might require exhaustive search and even luck to make it aggressive.

With a adequate variety of samples, there exists a minimum of one set of hyperparameters that leads to 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 completely different reminiscence traces for DRAMSys (DRAM subsystem design house exploration framework).

Dataset development 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 price fashions to enhance the velocity of structure simulation. To guage the advantages of datasets in constructing an ML mannequin to approximate structure price, we leverage ArchGym’s potential to log the info 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) Various Dataset, which represents the info collected from completely different brokers (ACO, GA, RW, and BO), and (b) ACO solely, which reveals the info collected solely from the ACO agent, each of that are launched together with ArchGym. We prepare 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:

  1. As we improve the dataset measurement, the common normalized root imply squared error (RMSE) barely decreases.
  2. Nevertheless, as we introduce variety within the dataset (e.g., accumulating knowledge from completely different brokers), we observe 9× to 42× decrease RMSE throughout completely different dataset sizes.

Various dataset assortment throughout completely different brokers utilizing ArchGym interface.
The affect of a various dataset and dataset measurement on the normalized RMSE.

The necessity for a community-driven ecosystem for ML-assisted structure analysis

Whereas, ArchGym is an preliminary effort in the direction of creating an open-source ecosystem that (1) connects a broad vary of search algorithms to laptop structure simulators in an unified and easy-to-extend method, (2) facilitates analysis in ML-assisted laptop structure, and (3) kinds the scaffold to develop reproducible baselines, there are plenty of open challenges that want community-wide help. Under we define a few of the open challenges in ML-assisted structure design. Addressing these challenges requires a nicely coordinated effort and a neighborhood pushed ecosystem.

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 laptop structure analysis. If you’re fascinated with serving to form this ecosystem, please fill out the curiosity survey.


ArchGym is an open supply gymnasium for ML structure DSE and allows an standardized interface that may be readily prolonged to go well with completely different use instances. Moreover, ArchGym allows truthful and reproducible comparability between completely different ML algorithms and helps to determine stronger baselines for laptop structure analysis issues.

We invite the pc structure 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 setting for laptop structure analysis could be a big step ahead within the discipline and supply a platform for researchers to make use of ML to speed up analysis and result in new and progressive designs.


This blogpost relies on joint work with a number of co-authors at Google and Harvard College. We want to acknowledge and spotlight Srivatsan Krishnan (Harvard) who contributed a number of concepts to this undertaking 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 prefer 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 prefer to thank John Guilyard for the animated determine used on this submit. Amir Yazdanbakhsh is now a Analysis Scientist at Google DeepMind and Vijay Janapa Reddi is an Affiliate Professor at Harvard.


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