Browsing by Author "Zhang, Yongan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Automated Deep Learning Algorithm and Accelerator Co-search for Both Boosted Hardware Efficiency and Task Accuracy
    (2023-04-24) Zhang, Yongan; Lin, Yingyan
    Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered intelligence into numerous applications. Jointly optimizing the networks and their accelerators are promising in providing optimal performance. However, the great potential of such solutions have yet to be unleashed due to the challenge of simultaneously exploring the vast and entangled, yet different design spaces of the networks and their accelerators. To this end, we propose DIAN, a DIfferentiable Accelerator-Network co-search framework for automatically searching for matched networks and accelerators to maximize both the task accuracy and acceleration efficiency. Specifically, DIAN integrates two enablers: (1) a generic design space for DNN accelerators that is applicable to both FPGA- and ASIC-based DNN accelerators and compatible with DNN frameworks such as PyTorch to enable algorithmic exploration for more efficient DNNs and their accelerators; and (2) a joint DNN network and accelerator co-search algorithm that enables the simultaneous search for optimal DNN structures and their accelerators’ micro-architectures and mapping methods to maximize both the task accuracy and acceleration efficiency. Experiments and ablation studies based on FPGA measurements and ASIC synthesis show that the matched networks and accelerators generated by DIAN consistently outperform state-of-the-art (SOTA) DNNs and DNN accelerators (e.g., 3.04× better FPS with a 5.46% higher accuracy on ImageNet), while requiring notably reduced search time (up to 1234.3×) over SOTA co-exploration methods, when evaluated over ten SOTA baselines on three datasets.
  • Thumbnail Image
    Item
    RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms
    (ACM, 2022) Zhang, Yongan; Banta, Anton; Fu, Yonggan; John, Mathews M.; Post, Allison; Razavi, Mehdi; Cavallaro, Joseph; Aazhang, Behnaam; Lin, Yingyan
    There exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable Real-Time and high-quality Reconstruction of ECG signals from EGM signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.