Browsing by Author "Lin, Cheng-Chung"

Now showing 1 - 11 of 11

A 0.42V Vccmin ASIC-Compatible Pulse-Latch Solution as a Replacement for a Traditional Master-Slave Flip-Flop in a Digital SOC
(2014)
Dhong, Sang
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Guo, Richard
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Kuo, Ming-Zhang
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Yang, Ping-Lin
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Lin, Cheng-Chung
We present a pulse latch with a measured Vccmin at the circuit of 0.42 V and pulse width of approximately 3 FO4-inverter delays. A wider operating window and reduced dependence on the input rise-time and PVT variations were obtained using a new pulse generator. A pulse in the new generator starts when its input crosses the switching level of its input gate, unlike in the classic text-book-style pulse-generator. An 8 to 10% improvement in power, performance, and area (PPA) of a typical digital SOC is observed when a group of pulse latches is driven by a distributed clock regenerator (OCR). The OCR has the new pulse generator at its input stage and provides pulse clocks to the pulse latches. Experimental results in a 28-nm HKMG process closely match SPICE simulations.
A 16kB Tile-able SRAM Macro Prototype for an Operating Window of 4.8GHz at 1.12V VDD to 10 MHz at 0.5V in a 28-nm HKMG CMOS
(2014)
Kuo, Ming-Zhang
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Hsieh, Henry
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Dhong, Sang
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Yang, Ping-Lin
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Lin, Cheng-Chung
This paper describes a tile-able 16-kByte 6-T SRAM macro in a High-K Metal-Gate (HKMG) 28-nm bulk technology with an operating window from 4.8 GHz at 1.12 V VDD down to 10 MHz at 0.5V, meeting almost all of the Dynamic Voltage Frequency Scaling (DVFS) requirements of Level-1 (L1) caches of a digital microprocessor SOC. It uses an unmodified technology-supported 0.156um(2) high-current (HC) SRAM cell. Innovative and carefully optimized circuit solutions provide the wide operating range measured in hardware. We also discuss two circuit improvements, a cross-coupled PMOS-pair for each bitline-pair with mux readout and an independently-controlled precharge-and-write driver (ICPW), which gives a wider DVFS operating window with reduced sensitivities to Process-Voltage-Temperature (PVT) variations. Improved SRAM macros with new circuits have been designed and laid out and their performance and area verified in simulation.
A language modeling approach to atomic human action recognition
(2007)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
;
Lin, Cheng-Chung
Visual analysis of human behavior has generated considerable interest in the field of computer vision because it has a wide spectrum of potential applications. Atomic human action recognition is an important part of a human behavior analysis system. In this paper, we propose a language modeling framework for this task. The framework is comprised of two modules: a posture labeling module, and an atomic action learning and recognition module. A posture template selection algorithm is developed based on a modified shape context matching technique. The posture templates form a codebook that is used to convert input posture sequences into training symbol sequences or recognition symbol sequences. Finally, a variable-length Markov model technique is applied to learn and recognize the input symbol sequences of atomic actions. Experiments on real data demonstrate the efficacy of the proposed system.
A Local Feature-based Human Motion Recognition Framework
(2009)
Lai, Yu-Chun
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
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Chen, Jian-Ren
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Luo, Y. -F. Peter
In this paper, we propose a local feature-based human motion analysis framework Instead of using traditional analysis methods to characterize the global structure of human motion, we extract features directly from local regions that contain motion. To implement the above concept, we adopt the rules of visual attention theory, which assert that a human motion can be described simply by a set of local features comprised of spatial relationships rather than human postures. We select two kinds of features to represent the local variation of a human motion. First, we extract the long-term movement trend of the motion. The second feature is actually a set of rough features derived by sampling multi-scale moving edges. The two types of features are considered together during the recognition process. Our experiments demonstrate that the proposed approach can achieve very good recognition results.
An All-Digital Phase-Locked Loop with a Multi-Delay-Switching TDC
(2017-01-01)
Su, Chung-Cheng
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Lin, Cheng-Chung
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Hung, Chung-Chih
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電機工程學系
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Department of Electrical and Computer Engineering
This paper presents a low-power time-to-digital converter (TDC) with multi-delay-switching mechanism for ADPLL application. In order to achieve low power dissipation and low area occupation, a switching mechanism is proposed on TDC design. The proposed ADPLL achieves the frequency range from 150 MHz to 1.45 GHz and 18.4 ps peak-to-peak jitter at 800 MHz. The design has been implemented in 0.18 um CMOS process with an active area of 0.1088 mm(2) and the whole system consumes 8.41 mW at 800 MHz.
Custom 6-R, 2-or 4-W Multi-Port Register Files in an ASIC SOC with a DVFS Window of 0.5 V, 130 MHz to 0.96 V, 3.2 GHz in a 28-nm HKMG CMOS Technology
(2015)
Hsieh, Henry
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Dhong, Sang H.
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Lin, Cheng-Chung
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Kuo, Ming-Zhang
;
Tseng, Kuo-Feng
We describe custom 6R, 2/4W general-purpose register files (GRF) in an ASIC-based SOC implemented in a N28 CMOS technology, which has roughly a 2 similar to 3 X smaller area, 2 X faster speed, and 5 X lower power than a logic synthesized version. Synthesized and custom GRFs also have a different read behavior from static and dynamic circuitry used, respectively. This is addressed by modifying a bypass control block. Hardware showed a DVFS window of 0.5 V @ccuit, 130 MHz to 0.96 V, 3.2 GHz.
Learning Atomic Human Actions Using Variable-Length Markov Models
(2009-02-01)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
;
Lin, Cheng-Chung
Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into discrete symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.
LINEAR PRODUCTION GAME SOLUTION TO A PTZ CAMERA NETWORK
(2010)
Lai, Yu-Chun
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Liang, Yu-Ming
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Shih, Sheng-Wen
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
Reconfiguring the PTZ parameters of a camera network is an combinatorial optimization problem and computing the optimal solution is very time consuming. Therefore, existing methods can only provide sub-optimal solutions. In this paper, a nonlinear objective function for better utilizing the cameras to track multiple targets is proposed. Furthermore, it is shown that by expanding the unknown parameters and imposing new constraints, the nonlinear objective function can be converted into a linear production game (LPG) problem. Since an LPG possesses an optimal solution which can be evaluated with polynomial time, the proposed method is efficient and accurate. Computer simulations have been conducted and the results show that the proposed method is very promising.
Linear Production Game Solution to a PTZ Camera Network
(2013-05-01)
Lai, Yu-Chun
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Liang, Yu-Ming
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Shih, Sheng-Wen
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Lin, Cheng-Chung
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Liao, Hong-Yuan Mark
Reconfiguring the parameters of PTZ cameras in a surveillance system is a combinatorial optimization problem. Computing the optimal solution is very time consuming, and existing methods can only provide sub-optimal solutions. In this paper, we propose a non-linear objective function that better utilizes a camera network to track multiple targets. We also show that, by expanding the unknown parameters and imposing new constraints, the non-linear objective function can be converted into a linear production game (LPG) problem. Since an LPG yields an optimal solution that can be evaluated in polynomial time, the proposed method is efficient and accurate. The results of simulations and a real-world experiment demonstrate the proposed method's potential.
Segmentation of human body parts in video frames based on intrinsic distance
(2007)
Lai, Yu-Chun
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
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資訊工程學系
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Department of Computer Science
We propose an intrinsic-distance based segmentation approach for segmenting human body parts in video frames. First, since the human body can be seen as a set of articulated parts, we utilize the moving articulated attributes to identify body part candidate regions automatically. The candidate regions and the background candidate regions are generated by voting and assigned to the spatiotemporal volume, which is comprised of frames of the video. Then, the intrinsic distance is used to estimate the boundaries of each body part. Our intrinsic distance-based segmentation technique is applied in the spatiotemporal volume to extract the optimal boundaries of the intrinsic distance in a video and obtain segmented frames from the segmented volume. The segmented results show that the proposed approach can tolerate incomplete and imprecise candidate regions because it provides temporal continuity. Furthermore, it can reduce over growing in the original intrinsic distance-based algorithm, since it can handle ambiguous pixels. We expect that this research can provide an alternative to segmenting a sequence of body parts in a video.
Unsupervised Analysis of Human Behavior Based on Manifold Learning
(2009)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
;
Lin, Cheng-Chung
In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to atomic actions. Next, a dynamic time warping (DTW) approach is applied for clustering atomic action sequences. Finally, we use the clustering results to learn and classify atomic actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method.