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1. Efficient Process-in-Memory Architecture Design for Unsupervised GAN-based Deep Learning using ReRAM

   https://doi.org/10.1145/3299874.3319482  

   Chen, Fan ; Song, Linghao ; Li, Hai 'Helen' ( May 2019 , Great Lakes Symposium on VLSI)

   The ending of Moore’s Law makes domain-specific architecture as the future of computing. The most representative is the emergence of various deep learning accelerators. Among the proposed solutions, resistive random access memory (ReRAM) based process-inmemory (PIM) architecture is anticipated as a promising candidate because ReRAM has the capability of both data storage and in-situ computation. However, we found that existing solutions are unable to efficiently support the computational needs required by the training of unsupervised generative adversarial networks (GANs), due to the lack of the following two features: 1) Computation efficiency: GAN utilizes a new operator, called transposed convolution. It inserts massive zeros in its input before a convolution operation, resulting in significant resource under-utilization; 2) Data traffic: The data intensive training process of GANs often incurs structural heavy data traffic as well as frequent massive data swaps. Our research follows the PIM strategy by leveraging the energy-efficiency of ReRAM arrays for vector-matrix multiplication to enhance the performance and energy efficiency. Specifically, we propose a novel computation deformation technique that can skip zero-insertions in transposed convolution for computation efficiency improvement. Moreover, we explore an efficient pipelined training procedure to reduce on-chip memory access. The implementation of related circuits and architecture is also discussed. At the end, we present our perspective on the future trend and opportunities of deep learning accelerators. 

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2. EMAT: an efficient multi-task architecture for transfer learning using ReRAM

   https://doi.org/10.1145/3240765.3240805  

   Chen, Fan ; Li, Hai ( November 2018 , International Conference on Computer-Aided Design)

   The ending of Moore’s Law makes domain-specific architecture as the future of computing. The most representative is the emergence of various deep learning accelerators. Among the proposed solutions, resistive random access memory (ReRAM) based process-inmemory (PIM) architecture is anticipated as a promising candidate because ReRAM has the capability of both data storage and in-situ computation. However, we found that existing solutions are unable to efficiently support the computational needs required by the training of unsupervised generative adversarial networks (GANs), due to the lack of the following two features: 1) Computation efficiency: GAN utilizes a new operator, called transposed convolution. It inserts massive zeros in its input before a convolution operation, resulting in significant resource under-utilization; 2) Data traffic: The data intensive training process of GANs often incurs structural heavy data traffic as well as frequent massive data swaps. Our research follows the PIM strategy by leveraging the energy-efficiency of ReRAM arrays for vector-matrix multiplication to enhance the performance and energy efficiency. Specifically, we propose a novel computation deformation technique that can skip zero-insertions in transposed convolution for computation efficiency improvement. Moreover, we explore an efficient pipelined training procedure to reduce on-chip memory access. The implementation of related circuits and architecture is also discussed. At the end, we present our perspective on the future trend and opportunities of deep learning accelerators. 

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3. PIM-TGAN: A Processing-in-Memory Accelerator for Ternary Generative Adversarial Networks

   https://doi.org/10.1109/ICCD.2018.00048  

   Rakin, Adnan Siraj ; Angizi, Shaahin ; He, Zhezhi ; Fan, Deliang ( October 2018 , 2018 IEEE 36th International Conference on Computer Design (ICCD))

   Generative Adversarial Network (GAN) has emerged as one of the most promising semi-supervised learning methods where two neural nets train themselves in a competitive environment. In this paper, as far as we know, we are the first to present a statistically trained Ternarized Generative Adversarial Network (TGAN) with fully ternarized weights (i.e. -1,0,+1) to massively reduce the need for computation and storage resources in the conventional GAN structures. In the proposed TGAN, the computationally expensive convolution operations (i.e. Multiplication and Accumulation) in both generator and discriminator's forward path are converted into hardware-friendly Addition/Subtraction operations. Accordingly, we propose a Processing-in-Memory accelerator for TGAN called (PIM-TGAN) based on Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) computational sub-arrays to efficiently accelerate the training process of GAN within non-volatile memory. In addition, we propose a parallelism technique to further enhance the training efficiency of TGAN. Our device-to-architecture co-simulation results show that, with almost the same inception score to the baseline GAN with floating point number weights on different data-sets, the proposed PIM-TGAN can obtain ~25.6× better energy-efficiency and 22× speedup compared to GPU platform averagely, and, 9.2× better energy-efficiency and 5.4× speedup over the best processing-in-ReRAM accelerators. 

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4. RED: A ReRAM-based Efficient Accelerator for Deconvolutional Computation

   https://doi.org/10.1109/TCAD.2020.2981055  

   Li, Ziru ; Li, Bing ; Fan, Zichen ; Li, Hai ( March 2020 , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   Deconvolution is a key component in contemporary neural networks, especially generative adversarial networks (GANs) and fully convolutional networks (FCNs). Due to extra operations of deconvolution compared to convolution, considerable degradation of performance as well as energy efficiency is incurred when implementing deconvolution on the existing resistive random access memory (ReRAM)-based processing-in-memory (PIM) accelerators. In this work, we propose a ReRAM-based accelerator design, RED, for providing high-performance and low-energy deconvolution. We analyze the deconvolution execution on the existing ReRAM-based PIMs and utilize its interior computation pattern for design optimization. RED includes two major contributions: pixel-wise mapping scheme and zero-skipping data flow. Pixel-wise mapping scheme removes the zero insertion and performs convolutions over several ReRAM arrays and thus enables parallel computations with non-zero inputs. Zero-skipping data flow, assisted with customized input buffers design, enhances the computation parallelism and input data reuse. In evaluation, we compare RED against the existing ReRAM-based PIMs and CMOS-based counterpart with a variety of GAN and FCN models, each of which contains multiple deconvolution layers. The experimental results show that RED achieves a 4.0×-56.16× speedup and a 1.05×-18.17× energy efficiency improvement over previous related accelerator designs. 

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5. Ensuring electronic medical record simulation through better training, modeling, and evaluation

   https://doi.org/10.1093/jamia/ocz161  

   Zhang, Ziqi ; Yan, Chao ; Mesa, Diego A. ; Sun, Jimeng ; Malin, Bradley A. ( October 2019 , Journal of the American Medical Informatics Association)

   Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In this article, we improve EMR simulation through a novel pipeline that (1) enhances the learning model, (2) incorporates evaluation criteria for data utility that informs learning, and (3) refines the training process.

   We propose a new electronic health record generator using a GAN with a Wasserstein divergence and layer normalization techniques. We designed 2 utility measures to characterize similarity in the structural properties of real and simulated EMRs in the original and latent space, respectively. We applied a filtering strategy to enhance GAN training for low-prevalence clinical concepts. We evaluated the new and existing GANs with utility and privacy measures (membership and disclosure attacks) using billing codes from over 1 million EMRs at Vanderbilt University Medical Center.

   The proposed model outperformed the state-of-the-art approaches with significant improvement in retaining the nature of real records, including prediction performance and structural properties, without sacrificing privacy. Additionally, the filtering strategy achieved higher utility when the EMR training dataset was small.

   These findings illustrate that EMR simulation through GANs can be substantially improved through more appropriate training, modeling, and evaluation criteria.

    

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