Over the last few years, the gap between FPGA and ASIC performance levels have been narrowed due to the constant development of FPGA technology. The high performance, together with lower development costs and a shorter time-to-market, turns FPGA based platforms attractive for a huge range of applications, among them, Time-to-Digital Converters (TDCs). It is therefore important to analyse the evolution of FPGA-based TDCs, to better understand where the research efforts should be focused in the near future. This work presents and discusses the improvements on the FPGA-based TDC research, aiming to be a starting point for new studies on this field, with some guidelines for future research. A state-of-the-art literature review on FPGA-based TDC is presented, aiming to categorize and discuss the existing architectures. This discussion addresses architectures characteristics, limitations and areas of application.

Time-to-Digital Converters (TDC) are popular circuits in many applications, where high resolution time measurements are required, for example, in Positron Emission Tomography (PET). Besides its resolution, the TDC's linearity is also an important performance indicator, therefore calibration circuits usually play an important role on TDCs architectures. This paper presents an all-digital TDC implemented using Structured Datapath to reduce the need for calibration circuitry and cells custom design, without compromising the TDC's linearity. The proposed design is fully implementable using a Hardware Description Language (HDL) and enables a complete design flow automation, reducing both development time and system's complexity. The TDC is based on a Delay Locked Loop (DLL) paired with a coarse counter to increase measurement range. The proposed architecture and the design approach have proven to be efficient in developing a high resolution TDC with high linearity. The proposed TDC was implemented in TSMC 0.18 CMOS technology process achieving a resolution of 180ps, with Differential Non-Linearity (DNL) and Integral Non-Linearity (INL) under 0.6 LSB.

In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a broad variety of research works on models based on point clouds, standing out for being efficient and robust in their intended tasks, but they are also characterized by requiring point cloud processing times greater than the minimum required, given the risky nature of the application. This research work aims to provide a design and implementation of a hardware IP optimized for computing convolutions, rectified linear unit (ReLU), padding, and max pooling. This engine was designed to enable the configuration of features such as varying the size of the feature map, filter size, stride, number of inputs, number of filters, and the number of hardware resources required for a specific convolution. Performance results show that by resorting to parallelism and quantization approach, the proposed solution could reduce the amount of logical FPGA resources by 40 to 50%, enhancing the processing time by 50% while maintaining the deep learning operation accuracy.

This paper proposes a design methodology for a synthesizable, fully digital TDC architecture. The TDC was implemented using a hardware description language (HDL), which improves portability between platforms and technologies and significantly reduces design time. The proposed design flow is fully automated using TCL scripting and standard CAD tools configuration files. The TDC is based on a Tapped Delay Line architecture and explores the use of Structured Data Path (SDP) as a way to improve the TDL linearity by homogenizing the routing and parasitic capacitances across the multiple TDL’s steps. The studied approach also secures a stable, temperature independent measurement operation. The proposed TDC architecture was fabricated using TSMC 180nm CMOS process technology, with a 50MHz reference clock and a supply voltage of 1.8V. The fabricated TDC achieved an 111ps RMS resolution and a single-shot precision of 54ps (0.48 LSB) and 279ps (2.51 LSB), with and without post-measurement software calibration, respectively. The DNL across the channel is mostly under 0.3 LSB and a maximum of 8 LSB peak-to-peak INL was achieved, when no calibration is applied.

Due to a point cloud’s sparse nature, a sparse convolution block design is necessary to deal with its particularities. Mechanisms adopted in computer vision have recently explored the advantages of data processing in more energy-efficient hardware, such as the FPGA, as a response to the need to run these algorithms on resource-constrained edge devices. However, implementing it in hardware has not been properly explored, resulting in a small number of studies aimed at analyzing the potential of sparse convolutions and their efficiency on resource-constrained hardware platforms. This article presents the design of a customizable hardware block for the voting convolution. We carried out an in-depth analysis to determine under which conditions the use of the voting scheme is justified instead of dense convolutions. The proposed hardware design achieves an energy consumption about 8.7 times lower than similar works in the literature by ignoring unnecessary arithmetic operations with null weights and leveraging data dependency. Access to data memory was also reduced to the minimum necessary, leading to improvements of around 55% in processing time. To evaluate both the performance and applicability of the proposed solution, the voting convolution was integrated into the well-known PointPillars model, where it achieves improvements between 23.05% and 80.44% without a significant effect on detection performance.