An FPGA-Based 1-GHz, $128\times128$  Cross-Correlator for Aperture Synthesis Imaging

Asif, Muhammad; Guo, Xiangzhou; Hu, Anyong; Miao, Jungang

doi:10.1109/tvlsi.2019.2937990

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Temporal decoding is achieved by correlating the sampled echo signal e(t) with each OOC codeword ci(t), i.e. 〈𝑒𝑒, 𝑐𝑐 𝑖𝑖 〉 = ∫ 𝑒𝑒(𝑡𝑡 ′ − 𝑡𝑡)𝑐𝑐 𝑖𝑖 (𝑡𝑡 ′ )d𝑡𝑡 ′ , which can be calculated efficiently and parallelly with current hardware 27,28 . Due to the orthogonality of OOCs, the auto-correlation signal exhibits a high peak value, whose position corresponding to the arriving time of the codeword, while the cross-correlation signals corresponding to other OCDM channels show low peak values.…”

Section: / 16mentioning

confidence: 99%

Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit

Zang

Luo

et al. 2021

Preprint

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Light detection and ranging (LiDAR) has been widely used in autonomous driving and large-scale manufacturing. Although state-of-the-art scanning LiDAR can perform long-range three-dimensional imaging, the frame rate is limited by both round-trip delay and the beam steering speed, hindering the development of high-speed autonomous vehicles. For hundred-meter level ranging applications, a several-time speedup is highly desirable. Here, we uniquely combine fiber-based encoders with wavelength-division multiplexing devices to implement all-optical time-encoding on the illumination light. Using this method, parallel detection and fast inertia-free spectral scanning can be achieved simultaneously with single-pixel detection. As a result, the frame rate of a scanning LiDAR can be multiplied with scalability. We demonstrate a 4.4-fold speedup for a maximum 75-m detection range, compared with a time-of-flight-limited laser ranging system. This approach has the potential to improve the velocity of LiDAR-based autonomous vehicles to the regime of hundred kilometers per hour and open up a new paradigm for ultrafast-frame-rate LiDAR imaging.

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Section: / 16mentioning

confidence: 99%

Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit

Zang

Luo

et al. 2021

Preprint

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“…However, they are less flexible and hard for end users to reconfigure. Several custom, open-source FPGA correlator designs have been made that provide much greater flexibility, but they have not yet been applied to DCS applications [44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

A Device-on-Chip Solution for Real-Time Diffuse Correlation Spectroscopy Using FPGA

Moore,

Sunar,

Lin

2024

Preprint

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Diffuse correlation spectroscopy (DCS) is a non-invasive technology for the evaluation of the blood perfusion in deep tissue. However, it requires high computational resources for data analysis which poses challenges in its implementation for real-time applications. To address the unmet need, we developed a novel device-on-chip solution that fully integrates all the necessary computational components needed for DCS. It takes the output of a photon detector and determines the blood flow index (BFI). It is implemented on a Field Programmable Gate Array (FPGA) chip including a multi-tau correlator for the calculation of the temporal light intensity autocorrelation function and a DCS analyzer to perform the curve fitting operation that derives the BFI. The FPGA DCS system was evaluated against a lab-standard DCS system for both phantom and cuff-ischemia studies. The results indicate that the autocorrelation of the light correlation and BFI from both the FPGA DCS and the reference DCS matched well. Furthermore, the FPGA DCS system was able to achieve a measurement rate of 50 Hz and resolve pulsatile blood flow.

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“…Hardware correlators have been developed using field-programmable gate array technology to address the need. For example, multi-channel FPGA cross-correlators were used in aperture synthesis imaging systems [15][16][17]. The FPGA correlator alleviated the computational load in realtime ultrasound sensor systems using the complementary ultrasound pulse sequences for the enhancement of the signal-to-noise ratio [18].…”

Section: Introductionmentioning

confidence: 99%

FPGA Correlator for Applications in Embedded Smart Devices

Moore¹,

Lin²

2022

Biosensors

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Correlation has a variety of applications that require signal processing. However, it is computationally intensive, and software correlators require high-performance processors for real-time data analysis. This is a challenge for embedded devices because of the limitation of computing resources. Hardware correlators that use Field Programmable Gate Array (FPGA) technology can significantly boost computational power and bridge the gap between the need for high-performance computing and the limited processing power available in embedded devices. This paper presents a detailed FPGA-based correlator design at the register level along with the open-source Very High-Speed Integrated Circuit Hardware Description Language (VHDL) code. It includes base modules for linear and multi-tau correlators of varying sizes. Every module implements a simple and unified data interface for easy integration with standard and publicly available FPGA modules. Eighty-lag linear and multi-tau correlators were built for validation of the design. Three input data sets—constant signal, pulse signal, and sine signal—were used to test the accuracy of the correlators. The results from the FPGA correlators were compared against the outputs of equivalent software correlators and validated with the corresponding theoretical values. The FPGA correlators returned results identical to those from the software references for all tested data sets and were proven to be equivalent to their software counterparts. Their computation speed is at least 85,000 times faster than the software correlators running on a Xilinx MicroBlaze processor. The FPGA correlator can be easily implemented, especially on System on a Chip (SoC) integrated circuits that have processor cores and FPGA fabric. It is the ideal component for device-on-chip solutions in biosensing.

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An FPGA-Based 1-GHz, $128\times128$ Cross-Correlator for Aperture Synthesis Imaging

Cited by 5 publications

References 44 publications

Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit

Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit

A Device-on-Chip Solution for Real-Time Diffuse Correlation Spectroscopy Using FPGA

FPGA Correlator for Applications in Embedded Smart Devices

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