Sunday, May 26, 2024

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

Pc Structure analysis has a protracted historical past of growing simulators and instruments to judge and form the design of laptop techniques. For instance, the SimpleScalar simulator was launched within the late Nineteen Nineties and allowed researchers to discover varied microarchitectural concepts. Pc structure simulators and instruments, comparable to gem5, DRAMSys, and lots of extra have performed a major function in advancing laptop structure analysis. Since then, these shared sources and infrastructure have benefited trade and academia and have enabled researchers to systematically construct on one another’s work, resulting in vital advances within the subject.

Nonetheless, laptop structure analysis is evolving, with trade and academia turning in the direction of machine studying (ML) optimization to satisfy stringent domain-specific necessities, comparable 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 deal with 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 a wide range 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 answer is essentially higher than one other. These outcomes additional point out that choosing the optimum hyperparameters for a given ML algorithm is important 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 answer for a DRAM controller) there isn’t a systematic solution to establish optimum ML algorithms or hyperparameters (e.g., studying fee, warm-up steps, and so on.). 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 needed 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, pace, and price in structure exploration. The accuracy and pace of efficiency estimation broadly 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 often undergo from excessive prediction error. Additionally, as a consequence of business 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 learn how to systematically evaluate the effectiveness of assorted ML algorithms beneath these constraints.
  3. Lastly, the panorama of ML algorithms is quickly evolving and a few ML algorithms want information to be helpful. Moreover, rendering the end result of DSE into significant artifacts comparable to datasets is vital for drawing insights concerning the design area.
    On this quickly evolving ecosystem, it’s consequential to make sure learn how to amortize the overhead of search algorithms for structure exploration. It isn’t obvious, nor systematically studied learn how to leverage exploration information 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 contains two most important elements: 1) the ArchGym setting and a pair of) the ArchGym agent. The setting is an encapsulation of the structure price mannequin — which incorporates latency, throughput, space, power, and so on., 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 may 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 elements, whereas additionally saving the exploration information because the ArchGym Dataset. At its core, the interface entails three most important indicators: {hardware} state, {hardware} parameters, and metrics. These indicators are the naked minimal to determine a significant communication channel between the setting and the agent. Utilizing 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, comparable to efficiency, power consumption, and so on. 

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

ML algorithms may very well be equally favorable to satisfy user-defined goal specs

Utilizing ArchGym, we empirically show that throughout completely different optimization aims 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} specific 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 establish the very best reward. Nevertheless, be aware that discovering the proper 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 variety 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 area 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 pace of structure simulation. To judge the advantages of datasets in constructing an ML mannequin to approximate structure price, we leverage ArchGym’s means to log the information from every run from DRAMSys to create 4 dataset variants, every with a special variety of information factors. For every variant, we create two classes: (a) Various Dataset, which represents the information collected from completely different brokers (ACO, GA, RW, and BO), and (b) ACO solely, which exhibits the information 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 enhance the dataset dimension, the typical normalized root imply squared error (RMSE) barely decreases.
  2. Nevertheless, as we introduce range within the dataset (e.g., accumulating information 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 dimension 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) types the scaffold to develop reproducible baselines, there are a whole lot of open challenges that want community-wide assist. Under we define a number of the open challenges in ML-assisted structure design. Addressing these challenges requires a properly 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 deal with the long-standing open issues in making use of ML for laptop structure analysis. In case you are concerned about 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 circumstances. 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 can be a major 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 progressive designs.


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 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 assist, suggestions, and motivation for this work. We might additionally prefer 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.

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