Digital-Compute-in-memory (DCIM) has demonstrated significant energy and area efficiency in convolutional neural network (CNN) accelerators, particularly for high precision applications. However, to mitigate parasitic effects on word and bit lines, most DCIMs employ fine-grained multiply-accumulate operations, which introduces new challenges and opportunities but has not been widely explored. This paper proposes FDCA: a fine-grained digital-CIM based CNN accelerator with hybrid quantization and weight-stationary dataflow, in which the key contributions are :1) a hybrid quantization approach for CNNs leveraging hessian trace and approximation is utilized. This method incorporates the ratio of computation time and storage time into quantization, achieving high efficiency while maintaining accuracy; 2) a Cartesian Genetic Programming based approximate shift and accumulate with error compensation is proposed, where an approximate adder tree is generated to compensate for errors introduced by DCIM; 3) an optimized weight-stationary dataflow is used to improve the utilization of CIM and eliminate dataflow stalls. The experimental results demonstrate that under 28-nm process, when running VGG16 and ResNet50 on CIFAR100, the proposed FDCA achieves 17.1TOPS/W and 18.79TOPS/W with only a slight decrease in accuracy by 0.71% and 0.98%, respectively. Compared to previous works, this work achieves 1.76× and 1.28× better in energy efficiency with less accuracy loss.

With the development of convolutional neural networks (CNN) across various domains, the growth in network structure complexity and computational load has increasingly become a research focus in the deployment of neural networks. The key to current research on neural network accelerators lies in striking a balance between computational accuracy and energy efficiency. This paper proposes a software-hardware co-design to strike the balance for CNN edge applications. On the hardware side, a 3-dimensional tensor engine (3D-TE), achieved with reconfigurable Tensor Processing Units (TPUs), is introduced for efficient convolution computation. We optimize the CNN dataflow on 3D-TE using a dimension reshaping method for feature maps rearrangement, and a double stationary dataflow scheduling to reduce memory access. This paper adopts a configurable approximate multiplier design based on Boolean Matrix Factorization (BMF) based logic synthesis applied in the architecture of TPU. The proposed 3D-TE, characterized by its configurable precision, enables the TPUs to dynamically adapt the bitwidth of features and weights in response to varying precision requirements. On the software side, a hessian-guided layer precision mapping is adopted to reduce unnecessary computational overhead, and a progressive re-training approach is proposed to enable a better approximation configuration and higher power reduction. Fabricated on 28-nm CMOS, this work achieves an optimized energy efficiency of 14.9 TOPS/W and 12.1 TOPS/W for ResNet56 and MobileNetV2 respectively, with 0.6V supply voltage and 150MHz clock frequency, representing an improvement of$1.33\times \sim 8.28\times $over the state-of-the-art works.

Approximate circuits sacrifice accuracy in exchange for energy efficiency and have been widely used in hardware deployment of neural networks (NNs). Since convolution accounts for most of the power consumption in NNs, it is necessary to design an approximate multiplication and accumulation (MAC) unit which improve the energy efficiency of hardware with ignorable accuarcy loss. In this work, an error-compensation-based energy-efficient MAC unit is proposed in which approximate multipliers are designed by Boolean matrix factorization and approximate adders are generated by Cartesian genetic programming. The proposed MAC unit is conducted on CIFAR10 using ResNet-18, where PDP is reduced by 58.8% with an accuracy loss of 0.81%.

Hand detection is an essential step to support many tasks including HCI applications. However, detecting various hands robustly under conditions of cluttered backgrounds, motion blur or changing light is still a challenging problem. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection yet at a high computational expense. In this paper, we propose a light CNN network, which uses a modified MobileNet as the feature extractor in company with the SSD framework to achieve a robust and fast detection of hand location and orientation. The network generates a set of feature maps of various resolutions to detect hands of different sizes. In order to improve the robustness, we also employ a top-down feature fusion architecture that integrates context information across levels of features. For an accurate estimation of hand orientation by CNN, we manage to estimate two orthogonal vectors' projections along the horizontal and vertical axes then recover the size and orientation of a bounding box exactly enclosing the hand. Evaluated on the challenging Oxford hand dataset, our method reaches 83.2% average precision (AP) at 139 FPS on a Nvidia Titan X, outperforming the previous methods both in accuracy and efficiency.