Due to the escalating demand to analyze large graphs, many organizations are now collecting billion-level property graph datasets, concurrently executing many complex graph queries against them, and expecting interactive-level response latency. However, such requirements are particularly challenging because of the notoriously irregular data access pattern and complex dependencies between heterogeneous subtasks. Despite the widespread availability of many-core CPUs and high-speed networking in modern datacenters, existing distributed graph query systems struggle with their inherent inefficiencies, resulting in low hardware utilization and poor query performance on these state-of-the-art hardware. To address these challenges, we introduce the Partitioned Stateful Traversal Machine (PSTM), which extends the Gremlin graph traversal machine. PSTM retains the expressive power of the Gremlin query language, enabling it to accommodate a wide range of graph query tasks, including traversal, pattern matching, filtering, and result aggregation. It additionally introduces query memoranda, allowing for more efficient implementation and execution of numerous graph queries in distributed environments. Moreover, PSTM facilitates various system-level optimizations, such as massively parallel execution, overlapping computation with communication, locality-aware data access, and lightweight progress tracking. Building upon PSTM, we develop GraphDance, a distributed graph database featuring an efficient asynchronous PSTM run-time. Our evaluations, conducted on an 8-node cluster, show that GraphDance achieves millisecond-level query latency for complex queries on terabyte-scale graphs, with an average latency reduction of 89.2% across all interactive complex queries in the LDBC SNB benchmark compared to existing distributed graph query systems.

This paper presents a system on chip (SoC) designed for high-density transcranial electrical stimulation (HD-TES) with real-time bio-impedance sensing. Multiple chips can work collaboratively to generate arbitrary waveforms for HD-TES including temporal interference (TI) stimulation. This design implements a combined digital calibration and analog switch mechanism for common-mode voltage holding (CMVH), ensuring long-time safety of HD-TES. The SoC can perform impedance measurement (IM) through transcranial alternating current stimulation (tACs) allowing to monitor the biological impedance variations during tACs. To ensure the safety of current stimulation, switched-capacitor overcurrent protection (OCP) circuit is integrated. Measured results show that the SoC can apply arbitrary stimulation currents within the voltage range of ±13V. The amplitude range of the current is from - 2.5mA to 2.5mA with a 10 μA minimum step. The common-mode voltage of the electrode remains stable near 0V when operating in HD-TES mode. Impedance measurement error is less than 5% within the extensive range of 0.5kΩ to 500kΩ.

We propose a novel scalable Bayesian optimization method with truncated subspace sampling (tSS-BO) to tackle high-dimensional optimization challenges for large-scale analog circuit sizing. To address the high-dimensional challenges, we propose subspace sampling subject to a truncated Gaussian distribution. This approach limits the effective sampling dimensionality down to a constant upper bound, independent of the original dimensionality, leading to a significant reduction in complexity associated with the curse of dimensionality. The distribution covariance is iteratively updated using a truncated flow, where approximate gradients and center steps are integrated with decaying prior subspace features. We introduce gradient sketching and local Gaussian process (GP) models to approximate gradients without additional simulations to mitigate systematic errors. To enhance efficiency and ensure compatibility with constraints, we utilize local GP models for the selection of promising candidates, avoiding the cost of acquisition function optimization. The proposed tSS-BO method exhibits clear advantages over state-of-the-art methods in experimental comparisons. In synthetic benchmark functions, the tSS-BO method achieves up to$4.93\times$evaluation speedups and a remarkable over$30\times$algorithm complexity reduction compared to the Bayesian baseline. In real-world analog circuits, our method achieves up to$2\times$speedups in simulation number and runtime.

DAC mismatch is a significant error in NS-SAR ADC. It introduces an essentially nonlinear behavior and causes harmonic distortion of the signal. In this paper, we propose an improved foreground digital calibration method. This method is combined with noise shaping technology, improves calibration accuracy and eliminates the impact of error accumulation on high-bit weights. Thus, it elegantly solves the harmonic distortion caused by the capacitor mismatch. Behavioral simulation of the improved foreground digital calibration method is demonstrated in a 12-bit prototype NS-SAR ADC. As a result, the NS-SAR ADC performance ENOB achieves 17.2-bit at 32 × OSR. Compared with conventional foreground calibration method, the SNDR increases from 81.4 dB to 106 dB and the SFDR increases from 86.2 dB to 117.4 dB. A 200-point Monte Carlo simulation demonstrates the robustness of the proposed calibration method.

IoT-empowered wearable multimodal monitoring system (IEMS), an IEMS for neurocritical care is developed to perform simultaneous monitoring of 8-channel electroencephalogram (EEG), 2-channel regional cerebral oxygen saturation (rSO2) based on near-infrared spectrum (NIRS), body surface temperature, electrocardiogram (ECG), photoplethysmography (PPG), and bioimpedance (Bio-Z). The IoT platform and wireless devices enable the patients’ signals available for remote diagnosis. Besides, analysis functions and artificial intelligence (AI) algorithms could be embedded in both the bedside platform and the cloud server to support physicians with clinical decisions. In the multimodal neural monitoring device, the following designs are adopted to face the neurological intensive care unit (NICU) application. Active electrodes and preamplifying free topology provide better signal quality and a larger dynamic range (DR). Dedicated low-power designs ensure the device lasts 10 h of operation. A nonwoven headset improves long-term wearability, which is also quick and easy to install. In the cardiovascular patch, the disposable electrode patch based on elastic materials ensures tight and comfortable contact with skin. Besides, the reusable wireless sensing module is tiny (20 mm$\times16$mm$\times9$mm) but could measure ECG, PPG, and Bio-Z simultaneously. Electrical tests and human subject (healthy volunteers and NICU patients) studies were conducted to examine the performance. The EEG channels show 130.75-dB DR and 0.84-$\mu {}\text{V}_{\mathrm{ RMS}}$input-referred noise, which also yields signals with high quality during human EEG monitoring. The NIRS channels exhibit good linearity and are able to operate under severe ambient interference. The temperature sensors show ±0.2 °C accuracy. Moreover, the system complies with mandatory standards for medical equipment. Overall, an IEMS can meet the requirements for NICU applications and could provide better comfort during long-term wearing.

This paper presents an integrated pulsed laser driver in 180nm BCD process for indirect time-of-flight applications. Pulses with 4A peak current at a modulation frequency up to 200MHz can be generated. Automatic optical power control (AOPC) is realized for laser eye safety protection. A DLL-based time interpolator is proposed to eliminate the mechanical moving components used during system calibration. The measured absolute accuracy of the time interpolator is +/−50ps with a resolution of 800ps and tuning range of 49. 6ns. This work effectively extends the measurement distance of the I-ToF system. The calibration efficiency is also improved with the proposed method.

Nanopore-based DNA sequencing technology has become one of the most promising sequencing approaches with its advantages of label-free and low cost. However, most of the biosensor systems for nanopore sequencing only perform passive detection which is merely part of the overall function of a practical DNA sequencing platform. In this paper, a multifunctional automated integrated CMOS platform for nanopore-based DNA sequencing is presented. The platform equipped with 16*8 biosensor array for nanopore detection is also able to perform bilayer lipid membrane capacitance detection and nanopore insertion pulse generation, realizing the whole process automated auxiliary function from transducer preparation to DNA sequencing. Post layout simulation shows that each cell consumes only 37.366μW while the whole system occupying 1.633mm2.

Abstract Intra-query fault tolerance has increasingly been a concern for online analytical processing, as more and more enterprises migrate data analytical systems from mainframes to commodity computers. Most massive parallel processing (MPP) databases do not support intra-query fault tolerance. They may suffer from prolonged query latency when running on unreliable commodity clusters. While SQL-on-Hadoop systems can utilize the fault tolerance support of low-level frameworks, such as MapReduce and Spark, their cost-effectiveness is not always acceptable. In this paper, we propose a smart intra-query fault tolerance (SIFT) mechanism for MPP databases. SIFT achieves fault tolerance by performing checkpointing, i.e., materializing intermediate results of selected operators. Different from existing approaches, SIFT aims at promoting query success rate within a given time. To achieve its goal, it needs to: (1) minimize query rerunning time after encountering failures and (2) introduce as less checkpointing overhead as possible. To evaluate SIFT in real-world MPP database systems, we implemented it in Greenplum. The experimental results indicate that it can improve success rate of query processing effectively, especially when working with unreliable hardware.