Conventional face image generation using generative adversarial networks (GAN) is limited by the quality of generated images since generator and discriminator use the same backpropagation network . In this paper, we discuss algorithms that can improve the quality of generated images, that is, high-quality face image generation . In order to achieve stability of network, we replace MLP with convolutional neural network (CNN) and remove pooling layers. We conduct comprehensive experiments on LFW, CelebA datasets and experimental results show the effectiveness of our proposed method.

Training large scale convolutional neural networks (CNNs) is an extremely computation and memory intensive task that requires massive computational resources and training time. Recently, many accelerator solutions have been proposed to improve the performance and efficiency of CNNs. Existing approaches mainly focus on the inference phase of CNN, and can hardly address the new challenges posed in CNN training: the resource requirement diversity and bidirectional data dependency between convolutional layers (CVLs) and fully-connected layers (FCLs). To overcome this problem, this paper presents a new accelerator architecture for CNN training, called TNPU, which leverages the complementary effect of the resource requirements between CVLs and FCLs. Unlike prior approaches optimizing CVLs and FCLs in separate way, we take an alternative by smartly orchestrating the computation of CVLs and FCLs in single computing unit to work concurrently so that both computing and memory resources will maintain high utilization, thereby boosting the performance. We also proposed a simplified out-of-order scheduling mechanism to address the bidirectional data dependency issues in CNN training. The experiments show that TNPU achieves a speedup of 1.5x and 1.3x, with an average energy reduction of 35.7% and 24.1% over comparably provisioned state-of-the-art accelerators (DNPU and DaDianNao), respectively.

Convolutional Neural Networks (CNNs) have been widely used in machine learning tasks. While delivering state-of-the-art accuracy, CNNs are known as both compute- and memory-intensive. This paper presents the SqueezeFlow accelerator architecture that exploits sparsity of CNN models for increased efficiency. Unlike prior accelerators that trade complexity for flexibility, SqueezeFlow exploits concise convolution rules to benefit from the reduction of computation and memory accesses as well as the acceleration of existing dense architectures without intrusive PE modifications. Specifically, SqueezeFlow employs a PT-OS-sparse dataflow that removes the ineffective computations while maintaining the regularity of CNN computations. We present a full design down to the layout at 65 nm, with an area of 4.80 mm2and power of 536.09 mW. The experiments show that SqueezeFlow achieves a speedup of 2:9× on VGG16 compared to the dense architectures, with an area and power overhead of only 8.8 and 15.3 percent, respectively. On three representative sparse CNNs, SqueezeFlow improves the performance and energy efficiency by 1:8× and 1:5× over the state-of-the-art sparse accelerators.

There are two approaches to improve the performance of Convolutional Neural Networks (CNNs): 1) accelerating computation and 2) reducing the amount of computation. The acceleration approaches take the advantage of CNN computing regularity which enables abundant fine-grained parallelisms in feature maps, neurons, and synapses. Alternatively, reducing computations leverages the intrinsic sparsity of CNN neurons and synapses. The sparsity represents as the computing “bubbles”, i.e., zero or tiny-valued neurons and synapses. These bubbles can be removed to reduce the volume of computations. Although distinctly different from each other in principle, we find that the two types of approaches are not orthogonal to each other. Even worse, they may conflict to each other when working together. The conditional branches introduced by some bubble-removing mechanisms in the original computations destroy the regularity of deeply nested loops, thereby impairing the intrinsic parallelisms. Therefore, enabling the synergy between the two types of approaches is critical to arrive at superior performance. This paper proposed a relaxed synchronous computing architecture, FlexFlow-Pro, to fulfill this purpose. Compared with the state-of-the-art accelerators, the FlexFlow-Pro gains more than 2.5× performance on average and 2× energy efficiency.

Neural networks (NNs) have achieved great success in a broad range of applications. As NN-based methods are often both computation and memory intensive, accelerator solutions have been proved to be highly promising in terms of both performance and energy efficiency. Although prior solutions can deliver high computational throughput for convolutional layers, they could incur severe performance degradation when accommodating the entire network model, because there exist very diverse computing and memory bandwidth requirements between convolutional layers and fully connected layers and, furthermore, among different NN models. To overcome this problem, we proposed an elastic accelerator architecture, called SynergyFlow, which intrinsically supports layer-level and model-level parallelism for large-scale deep neural networks. SynergyFlow boosts the resource utilization by exploiting the complementary effect of resource demanding in different layers and different NN models. SynergyFlow can dynamically reconfigure itself according to the workload characteristics, maintaining a high performance and high resource utilization among various models. As a case study, we implement SynergyFlow on a P395-AB FPGA board. Under 100MHz working frequency, our implementation improves the performance by 33.8% on average (up to 67.2% on AlexNet) compared to comparable provisioned previous architectures.

Quality management, which is responsible for controlling approximation quality to meet user requirement, plays a key role in the applicability of approximate computing. An effective and efficient quality management needs to be accurate to detect intolerable errors meanwhile light-weight in nature. However, it is difficult to design such a quality management satisfying both the two demands and existing work usually optimizes for one demand at the expense of the other. In this paper, we aim to achieve higher energy efficiency of quality management by optimizing detection accuracy and overhead simultaneously. We observe that the detection difficulty varies across inputs and there exists much redundant computation in detection process. Based on this observation, a cascaded quality management which can minimize the overhead and doesn't lower detection accuracy is proposed. The proposed solution pays more proper computation effort according to different detection difficulties of inputs so as to avoid unnecessary energy consumption. What's more, by exploring the design space sufficiently and effectively, we can assure the highest energy-efficiency of the proposed topology. The experiment results demonstrate that our approach can achieve much greater energy-efficiency than existing solutions.

Convolutional Neural networks (CNNs) have achieved great success in a broad range of applications. As CNN-based methods are often both computation and memory intensive, sparse CNNs have emerged as an effective solution to reduce the amount of computation and memory accesses while maintaining the high accuracy. However, dense CNN accelerators can hardly benefit from the reduction of computations and memory accesses due to the lack of support for irregular and sparse models. This paper proposed a concise convolution rule (CCR) to diminish the gap between sparse CNNs and dense CNN accelerators. CCR transforms a sparse convolution into multiple effective and ineffective ones. The ineffective convolutions in which either the neurons or synapses are all zeros do not contribute to the final results and the computations and memory accesses can be eliminated. The effective convolutions in which both the neurons and synapses are dense can be easily mapped to the existing dense CNN accelerators. Unlike prior approaches which trade complexity for flexibility, CCR advocates a novel approach to reaping the benefits from the reduction of computation and memory accesses as well as the acceleration of the existing dense architectures without intrusive PE modifications. As a case study, we implemented a sparse CNN accelerator, SparseK, following the rationale of CCR. The experiments show that SparseK achieved a speedup of 2.9× on VGG16 compared to a comparably provisioned dense architecture. Compared with state-of-the-art sparse accelerators, SparseK can improve the performance and energy efficiency by 1.8× and 1.5×, respectively.

Convolutional Neural Network (CNN) accelerators are rapidly growing in popularity as a promising solution for deep learning based applications. Though optimizations on computation have been intensively studied, the energy efficiency of such accelerators remains limited by off-chip memory accesses since their energy cost is magnitudes higher than other operations. Minimizing off-chip memory access volume, therefore, is the key to further improving energy efficiency. However, we observed that sticking to minimizing the accesses of one data type as many prior work did cannot fit the varying shapes of convolutional layers in CNNs. Hence, there exists a dilemma of minimizing the accesses of which data type. To overcome the problem, this paper proposed an adaptive layer partitioning and scheduling scheme, called SmartShuttle, to minimize off-chip memory accesses for CNN accelerators. Smartshuttle can adaptively switch among different data reuse schemes and the corresponding tiling factor settings to dynamically match different convolutional layers. Moreover, SmartShuttle thoroughly investigates the impact of data reusability and sparsity on the memory access volume. The experimental results show that SmartShuttle processes the convolutional layers at 434.8 multiply and accumulations (MACs)/DRAM access for VGG16 (batch size = 3), and 526.3 MACs/DRAM access for AlexNet (batch size = 4), which outperforms the state-of-the-art approach (Eyeriss) by 52.2% and 52.6%, respectively.