This paper presents a two-stage pipelined-SAR ADC with offset cancellation technique, which cancels the equivalent offset induced by the comparator and residue amplifier (RA) by forming a feedback path with a notch filter and level-shift circuits. Moreover, the ADC is calibrated on chip through one-time bit-weight calibration together with an independent path to track the interstage gain. Fabricated in a 40nm CMOS process, the prototype ADC achieves a peak SNDR of 66.7 dB and a SFDR of 87.5 dB at 100 MS/s, while consuming 3.7 mW from a 1.1 V supply. The measured Delta SNDR is less than 2.4 dB under +/- 5% supply voltage variation and -20 degrees C-125 degrees C temperature variation.
In this paper, we introduce a new method for shifting-in-memory exponent normalization and shifting-in-memory mantissa alignment. This approach allows us to add exponents and find the maximum sum simultaneously. We also replace the traditional subtraction and shifting for mantissa alignment with CA and shifting-in-memory operation, enabling FP-CIM to be achieved with lower delay, area, and power overheads. The macro is designed in TSMC 28nm process, with a memory size of 32Kb. Simulation results indicate that the computing core operates at a frequency of 280MHz at a 0.55V supply voltage, and achieves an energy efficiency of 13.5 TFLOPS/W.
In real-time video conferencing systems, webcams often apply image resizing methods, such as nearest-neighbor, bilinear, bicubic, and Lanczos interpolation, to highlight facial regions and enhance user experience. However, interpolations introduce significant high-frequency artifacts that distort perceived image quality. Our work discovered that existing state-of-theart image quality assessments (IQA) greatly overestimate the sharpness in images resized by nearest-neighbor interpolation, failing to extract useful information in the image's high-frequency components. To address this, we propose FRIEREN (Face Resizing Image Detail Quality Evaluation via Robust Estimation of Image Naturalness), a novel IQA for detail quality evaluation that integrates measures of image naturalness, including motion noise, spatial noise, and HVS-based sharpness. Designed features are fed to Kolmogorov-Arnold Networks (KANs) for quality prediction. Experimental results show that FRIEREN effectively and accurately evaluates the face image detail quality scaled by different interpolations with low computational complexity, making it suitable for quality-aware vision systems.
This work presents an energy-efficient multi-bit (8-b) current-based analog SRAM compute-in-memory (CIM) architecture for MAC operations. Our key idea is to group 8-bit inputs and weights into 2-bit segments for performing MAC operations. This improves signal margin without requiring external circuitry. We leverage a 1C-2C capacitive network for efficient weight encoding and also use an analog shift-and add technique for accumulating partial sums with only 1C and-4C capacitors, thus avoiding the need for higher capacitor values. This approach enhances charge redistribution efficiency and minimizes ADC overhead. The proposed design features a 64x128 6T SRAM array, which can perform 2048 8-bit MAC operations in four cycles with a latency of 15ns. The architecture is implemented in TSMC 65nm technology at 1.2V. The architecture maintains linear MAC operations across different input and weight cases and process corners. The proposed CIM macro delivers a throughput of 136.5 GOPS and MAC energy efficiency of 6.18 TOPS/W while maintaining a signal margin of 32 mV. Our approach achieves an inference accuracy of 98.62%, 90.38% and 69.46% on MNIST, CIFAR-10 and CIFAR-100, respectively.
This paper proposes an edge computing system for real-time arrhythmia classification in the elderly, leveraging a novel one-dimensional SqueezeNet optimized for ECG signal. The special design of Fire Module leverages point-wise (1x1) convolutions to compress channel dimensions and combines them with wider (3x1) convolutions to effectively balance parameter efficiency and temporal feature extraction. By directly receiving the raw input, unnecessary signal conversion operations are effectively avoided. The compact architecture (0.36 million parameters) attains 91.41% accuracy, 99.56% recall, 98.56% precision, and 99.06% F1 score on the MIT-BIH Arrhythmia Database. These results show better performance for classifying previously unseen data of the proposed model than the state-of-the-art works. Evaluated only on elderly subjects, accuracy rises to 96.25% without any decline in other performance metrics, emphasizing the necessity of conducting separate training for the ECG of the elderly. The trained model is quantized and deployed to an Arm Cortex-M7 processor, yielding an inference latency of 146 ms with only 121 kB RAM usage to enable on-device processing. This demonstrates that high-performance elderly-specific arrhythmia classification can be realized on resource-constrained edge computing system without cloud dependency.
