| 1. |
- Chen, Yizhi, 1995-, et al.
(författare)
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Online Image Sensor Fault Detection for Autonomous Vehicles
- 2022
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 120-127
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Konferensbidrag (refereegranskat)abstract
- Automated driving vehicles have shown glorious potential in the near future market due to the high safety and convenience for drivers and passengers. Image sensors' reliability attract many researchers' interests as many image sensors are used in autonomous vehicles. We propose an online image sensor fault detection method based on comparing the historical variances of normal pixels and defective pixels to detect faults. For fault pixels without uncertainty, with a detecting window of more than 30 frames, we get 100% accuracy and 100% recall on realistic continuous traffic pictures from the KITTI data set. We also explore the influence of fault pixel values' uncertainty from 0% to 25% and study different fixed thresholds and a dynamic threshold for judgments. Strict threshold, which is 0.1, has a high accuracy (99.16%) but has a low recall (34.46%) for 15% uncertainty. Loose threshold, which is 0.3, has a relatively high recall (83.78%) but mistakes too many normal pixels with 18.17% accuracy for 15% uncertainty. Our dynamic threshold balances the accuracy and recall. It gets 100% accuracy and 58.69% recall for 5% uncertainty and 78.38% accuracy and 55.39% recall for 15% uncertainty. Based on the detected damage pixel rate, we develop a health score for evaluating the image sensor system intuitively. It can also be helpful for making decision about replacing cameras.
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| 2. |
- Lu, Zhonghai, et al.
(författare)
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Wearable pressure sensing for lower limb amputees
- 2022
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Ingår i: BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665469173 ; , s. 105-109, s. 105-109
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Konferensbidrag (refereegranskat)abstract
- Pressure sensing in prosthetic sockets is valuable as it provides quantified data to assist prosthetists in designing comfortable sockets for amputees. We present a wearable pressure sensing system for lower limb amputees. The full system consists of three essential elements from sensing scheme (wearable sensors, sensor calibration and deployment), electronic measurement system (embedded hardware and software), to time-series database and visualization. The full system has been successfully applied in clinical trials to effectively collect pressure data in real-time.
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| 3. |
- Chen, Yizhi, 1995-, et al.
(författare)
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Accelerating Non-Negative Matrix Factorization on Embedded FPGA with Hybrid Logarithmic Dot-Product Approximation
- 2022
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 239-246
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Konferensbidrag (refereegranskat)abstract
- Non-negative matrix factorization (NMF) is an ef-fective method for dimensionality reduction and sparse decom-position. This method has been of great interest to the scien-tific community in applications including signal processing, data mining, compression, and pattern recognition. However, NMF implies elevated computational costs in terms of performance and energy consumption, which is inadequate for embedded applications. To overcome this limitation, we implement the vector dot-product with hybrid logarithmic approximation as a hardware optimization approach. This technique accelerates floating-point computation, reduces energy consumption, and preserves accuracy. To demonstrate our approach, we employ a design exploration flow using high-level synthesis on an embedded FPGA. Compared with software solutions on ARM CPU, this hardware implementation accelerates the overall computation to decompose matrix by 5.597 × and reduces energy consumption by 69.323×. Log approximation NMF combined with KNN(k-nearest neighbors) has only 2.38% decreasing accuracy compared with the result of KNN processing the matrix after floating-point NMF on MNIST. Further on, compared with a dedicated floating-point accelerator, the logarithmic approximation approach achieves 3.718× acceleration and 8.345× energy reduction. Compared with the fixed-point approach, our approach has an accuracy degradation of 1.93% on MNIST and an accuracy amelioration of 28.2% on the FASHION MNIST data set without pre-knowledge of the data range. Thus, our approach has better compatibility with the input data range.
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| 4. |
- Nevarez, Yarib, et al.
(författare)
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CNN Sensor Analytics With Hybrid-Float6 Quantization on Low-Power Embedded FPGAs
- 2023
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 4852-4868
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Tidskriftsartikel (refereegranskat)abstract
- The use of artificial intelligence (AI) in sensor analytics is entering a new era based on the use of ubiquitous embedded connected devices. This transformation requires the adoption of design techniques that reconcile accurate results with sustainable system architectures. As such, improving the efficiency of AI hardware engines as well as backward compatibility must be considered. In this paper, we present the Hybrid-Float6 (HF6) quantization and its dedicated hardware design. We propose an optimized multiply-accumulate (MAC) hardware by reducing the mantissa multiplication to a multiplexor-adder operation. We exploit the intrinsic error tolerance of neural networks to further reduce the hardware design with approximation. To preserve model accuracy, we present a quantization-aware training (QAT) method, which in some cases improves accuracy. We demonstrate this concept in 2D convolution layers. We present a lightweight tensor processor (TP) implementing a pipelined vector dot-product. For compatibility and portability, the 6-bit floating-point (FP) is wrapped in the standard FP format, which is automatically extracted by the proposed hardware. The hardware/software architecture is compatible with TensorFlow (TF) Lite. We evaluate the applicability of our approach with a CNN-regression model for anomaly localization in a structural health monitoring (SHM) application based on acoustic emission (AE). The embedded hardware/software framework is demonstrated on XC7Z007S as the smallest Zynq-7000 SoC. The proposed implementation achieves a peak power efficiency and run-time acceleration of 5.7 GFLOPS/s/W and 48.3x, respectively.
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