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- research-articleFebruary 2024
Explainable-DSE: An Agile and Explainable Exploration of Efficient HW/SW Codesigns of Deep Learning Accelerators Using Bottleneck Analysis
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 87–107https://doi.org/10.1145/3623278.3624772Effective design space exploration (DSE) is paramount for hardware/software codesigns of deep learning accelerators that must meet strict execution constraints. For their vast search space, existing DSE techniques can require excessive trials to obtain a ...
- research-articleFebruary 2024
VarSaw: Application-tailored Measurement Error Mitigation for Variational Quantum Algorithms
- Siddharth Dangwal,
- Gokul Subramanian Ravi,
- Poulami Das,
- Kaitlin N. Smith,
- Jonathan Mark Baker,
- Frederic T. Chong
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 362–377https://doi.org/10.1145/3623278.3624764For potential quantum advantage, Variational Quantum Algorithms (VQAs) need high accuracy beyond the capability of today's NISQ devices, and thus will benefit from error mitigation. In this work we are interested in mitigating measurement errors which ...
- research-articleFebruary 2024
RECom: A Compiler Approach to Accelerating Recommendation Model Inference with Massive Embedding Columns
- Zaifeng Pan,
- Zhen Zheng,
- Feng Zhang,
- Ruofan Wu,
- Hao Liang,
- Dalin Wang,
- Xiafei Qiu,
- Junjie Bai,
- Wei Lin,
- Xiaoyong Du
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 268–286https://doi.org/10.1145/3623278.3624761Embedding columns are important for deep recommendation models to achieve high accuracy, but they can be very time-consuming during inference. Machine learning (ML) compilers are used broadly in real businesses to optimize ML models automatically. ...
- research-articleFebruary 2024
LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 202–218https://doi.org/10.1145/3623278.3624757To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framework, which consists of (1) precise and differentiable optical physics ...
- research-articleFebruary 2024
DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 73–86https://doi.org/10.1145/3623278.3624753Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dynamic behaviors introduce new challenges to the system software in an ML ...
- research-articleFebruary 2024
Exploiting the Regular Structure of Modern Quantum Architectures for Compiling and Optimizing Programs with Permutable Operators
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 108–124https://doi.org/10.1145/3623278.3624751A critical feature in today's quantum circuit is that they have permutable two-qubit operators. The flexibility in ordering the permutable two-qubit gates leads to more compiler optimization opportunities. However, it also imposes significant challenges ...