IEEE transactions on biomedical circuits and systems最新文献

The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike packets are routed through a network-on-chip (NoC). However, the information can be lost in the NoC under high spike traffic conditions, leading to performance degradation. This work presents NEXUS, a 16-core SNN with a diamond-shaped NoC topology fabricated in 28-nm CMOS technology. It integrates 4096 leaky integrate-and-fire (LIF) neurons with 1M 4-bit synaptic weights, occupying an area of 2.16 mm2. The proposed NoC architecture is scalable to any network size, ensuring no data loss due to contending packets with a maximum routing latency of 5.1μs for 16 cores. The proposed congestion management method eliminates the need for FIFO in routers, resulting in a compact router footprint of 0.001 mm². The proposed neurosynaptic core allows for increasing the processing speed by up to 8.5× depending on input sparsity. The SNN achieves a peak throughput of 4.7 GSOP/s at 0.9 V, consuming a minimum energy per synaptic operation (SOP) of 3.3 pJ at 0.55 V. A 4-layer feed-forward network is mapped onto the chip, classifying MNIST digits with 92.3% accuracy at 8.4Kclassification/ s and consuming 2.7-μJ/classification. Additionally, an audio recognition task mapped onto the chip achieves 87.4% accuracy at 215-μJ/classification.

GCOC is a genome classification system-on-chip (SoC) that classifies genomes by k-mer matching, an approach that divides a DNA query sequence into a set of short DNA fragments of size k, which are searched in a reference genome database, with the underlying assumption that sequenced DNA reads of the same organism (or its close variants) share most of such k-mers. At the core of GCOC is a similarity, or approximate search-capable Content Addressable Memory (SAS-CAM), which in addition to exact match, also supports approximate, or Hamming distance tolerant search. Classification operation is controlled by an embedded RISC-V processor. GCOC classification platform was designed and manufactured in a commercial 65nm process. We conduct a thorough analysis of GCOC classification efficiency as well as its performance, silicon area, and power consumption using silicon measurements. GCOC classifies 769.2K short DNA reads/sec. The silicon area of GCOC SoC is 3.12mm² and its power consumption is 1.27mW. We envision GCOC deployed as a field (for example at points of care) portable classifier where the classification is required to be real-time, easy to operate and energy efficient.

This paper presents a 16×20 CMOS biosensor array based on electrochemical impedance spectroscopy (EIS), a highly sensitive label-free technique for rapid disease detection at point-of-care. This high-density system implements a polar-mode detection with phase-only EIS measurement over a 5 kHz - 1 MHz frequency range. The design features predominantly digital readout circuitry, ensuring scalability with technology, along with a load-compensated transimpedance amplifier at the front, all within a 140×140 μm²; pixel. The architecture enables in-pixel digitization and accumulation, which increases the SNR by 10 dB for each 10× increase in readout time. Implemented in a 180 nm CMOS process, the 3×4 mm² chip achieves state-of-the-art performance with an rms phase error of 0.035% at 100 kHz through a duty-cycle insensitive phase detector and one of the smallest per pixel areas with in-pixel quantization.

Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recognition process for continuous monitoring, which only reduces operation time but remains challenged by the high cost of additional hardware. To address this problem, this article proposes a novel fusion architecture for AI processors, which enables event-triggered cross-paradigm integration and computation. Our method introduces a distributed-aggregated classification architecture (D-ACA) that facilitates the reuse of hardware resources across two-stage recognition, thereby obviating the need for standby hardware and enhancing energy efficiency. Integrating a non-encoding biomedical circuit method based on spiking neural networks (SNNs), the architecture eliminates encoded neurons at the hardware level, significantly optimizing energy consumption and hardware resource utilization. Additionally, we develop a configurable and highly flexible control method that supports various neuron modules, enabling continuous detection of epileptic seizures and activating high-precision recognition upon event detection. Finally, we implement the design on the Xilinx ZCU 102 FPGA board, where the AI processor achieves a high classification accuracy of 98.1% while consuming extremely low classification energy (3.73 μJ per classification).

In recent years, pulsed magnetic field (PMF) have attracted significant attention as a non-invasive electroporation method in the biomedical field. To further explore the biomedical effects generated by oscillating PMF, we designed a novel PMF generator for biomedical research. Based on resonance principles, the designed generator outputs sinusoidal oscillating PMF. To validate the feasibility and application value of the designed topology, a miniaturized platform was constructed using a selected multi-turn solenoid coil. The output performance of the generator was tested under different discharge voltage levels. The results revealed that the current multiplication factor remained consistently around 2 times, with the energy efficiency and circuit quality factor maintained at 82% and above 4.5, respectively. In addition, the generator's ability to flexibly modulate the number of pulse oscillations was demonstrated. The compatibility of the designed coil parameters and generator circuit parameters was analyzed, with tests on the effects of coil resistance and switch action time on the generator's output performance. Based on the magnetic field action platform, a simulation model of the actual scale coil was established. The spatial and temporal distribution of the magnetic field, induced electric field, and power transmission in the target area were described from multiple angles. Finally, biological experiments conducted using the constructed generator revealed the synergistic effect of sinusoidal oscillating PMF combined with drugs in tumor cell killing.