Underwater image restoration is a challenging task due to color distortion and hazing effects caused by light absorption and scattering. We propose a novel image restoration architecture, the Intrinsic Supervision Shuffle Residual UNet (ISSR-UNet), based on the UNet framework. ISSR-UNet leverages Adaptive Selective Intrinsic Supervised Features and a cross-scale feature shuffling mechanism to enhance image restoration performance and preserve more image details. It incorporates four pivotal components: the Selective Residual (SR) block, the Cross-scale Feature Shuffling (CFS) module, the Multi-Degradation Supervision (MDS) module, and the Adaptive Selective Intrinsic Supervised Feature Extraction (ASISFE) module. Extensive experiments validate that ISSR-UNet achieves superior performance, surpassing state-of-the-art methods on underwater image restoration benchmarks.

Block Floating-Point (BFP), an emerging datatype, has demonstrated significant potential in model accuracy and hardware efficiency. This paper presents a dynamic mixedprecision BFP processing engine (PE), an accompanying framework, and optimization techniques to improve hardware efficiency. First, we propose a strategy for identifying accuracysensitive inner products within BFP models by comparing exponent values against predefined thresholds. This enables precision adjustments according to the sensitivity of the calculations at runtime. Second, we observe that only a small subset of inner products require full-precision (i.e., accuracy-sensitive inner products). Furthermore, a full-precision multiplication can be decomposed into four low-precision multiplications. Based on this, we propose the design of a low-precision PE capable of supporting full-precision mode, thereby reducing area overhead. Third, we optimize the BFP quantization scheme and datatype representation within the PE, significantly mitigating quantization errors in low-precision mode and reducing power consumption during datatype conversion. Finally, experimental results demonstrate that our dynamic mixed-precision BFP approach maintains accuracy while employing over 80% lowprecision operations and increases this ratio to 95% through retraining. Compared to state-of-the-art BFP architectures, our design improves inference speed, area efficiency, and energy efficiency by up to$1.64 \times, 1.42 \times$, and$1.47 \times$, respectively.

This paper proposes built-in self-test (BIST) and built-in self-repair (BISR) strategies for computing in memory (CIM), including a novel test method and two repair schemes. They all focus on mitigating the impacts of inherent and in-evitable CIM inaccuracy on convolution neural networks (CNNs). Regarding the proposed BIST strategy, it exploits the distributive law to achieve at-speed CIM tests without storing testing vectors or golden results. Besides, it can assess the severity of the inherent inaccuracies among CIM bitlines instead of only offering a pass/fail outcome. In addition to BIST, we propose two BISR strategies. First, we propose to slightly offset the dynamic range of CIM outputs toward the negative side to create a margin for negative noises. By not cutting CIM outputs off at zero, negative noises are preserved to cancel out positive noises statistically, and accuracy impacts are mitigated. Second, we propose to remap the bitlines of CIM according to our BIST outcomes. Briefly speaking, we propose to map the least noisy bitlines to be the MSBs. This remapping can be done in the digital domain without touching the CIM internals. Experiments show that our proposed BIST and BISR strategies can restore CIM to less than 1% Top-1 accuracy loss with slight hardware overhead.

To address the inefficiency resulting from data movement in Von Neumann architecture, computing-in-memory (CIM) is a promising solution due to its in-situ analog computation. Among the various types of CIMs, time-domain CIM stands out as a promising solution for achieving high energy efficiency and high readout resolution by employing time-to-digital converters (TDC) instead of analog-to-digital converters (ADC) to convert time-domain delays into digital values. However, the performance of the accelerator may be constrained by the maximum operating frequency of time-domain CIM, which is significantly lower than that of digital circuits.This paper proposes an architecture for a time-domain CIM-based neural network accelerator that leverages the varying output time of the TDC. The key contributions of this work are as follows: 1) We introduce an early-termination scheme for time-domain CIM, which dynamically determines the length of the CIM clock period by deriving the maximum possible multiply-accumulate (MAC) value based on the current input. This approach reduces computation time for low-MAC results. 2) We propose an input-inversion scheme to decrease the computation time for high-MAC results. By employing linear combination, we perform bit-inversion on large inputs and compensate for the results using a low-cost digital circuit. 3) We propose a hardware optimization on the compensation circuit by combining it with shift-adders in traditional neural network accelerators.Experiments show that our schemes could gain 2× ∼ 2.9× speedup under different clock period specifications with 5.82% area overhead compared to the CIM macro.

In the clinical diagnosis of mood disorder, a large proportion of patients with bipolar disorder (BD) are misdiagnosed as having unipolar depression (UD). Generally, long-term tracking is required for patients with BD to conduct an appropriate diagnosis by using traditional diagnosis tools. A one-time diagnosis system for facilitating diagnosis procedures is thus highly desirable. Accordingly, in this study, the facial expressions of patients with BD, patients with UD, and healthy controls elicited by emotional video clips were used for conducting mood disorder classification; the classification was performed by exploring the temporal fluctuation characteristics among the three groups. First, macroscopic facial expressions characterized by action units (AUs) were applied for describing the temporal transformation of muscles. Modulation spectrum analysis was applied to extract short-term intensity variations in the AUs. An interval-based multilayer perceptron (MLP) neural network was then used to classify mood disorder on the basis of the detected AU intensities. Moreover, motion vectors (MVs) were employed to describe subtle changes in facial expressions in the microscopic view. Eight basic orientations of MV change were considered for representing microfluctuation. Wavelet decomposition was then applied to extract entropy and energy features in different frequency bands. A long short-term memory model was finally used to model long-term variations for conducting mood disorder classification. A decision-level fusion approach was conducted on the combined results of macroscopic and microscopic facial expressions. For evaluating the described methods, the facial expressions elicited from the 36 subjects (12 from each of the BD, UD, and control groups) were used in 12-fold cross-validation experiments. Approaches for macroscopic and microscopic expressions achieved classification accuracies of 63.9 and 66.7 percent, respectively, and the accuracy of the fusion approach reached 72.2 percent. The results indicate that macroscopic and microscopic view descriptors are complementary to each other and helpful for conducting mood disorder classification.