Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

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Massive MIMO Detection Techniques: A Survey

Understand the fundamentals of wireless and MIMO communication with this accessible and comprehensive text. Viewing the subject through an information theory lens, but also drawing on other perspectives, it provides a sound treatment of the key concepts underpinning contemporary wireless communication and MIMO, all the way to massive MIMO. Authoritative and insightful, it includes over 330 worked examples and 450 homework problems, with solutions and MATLAB code and data available online. Altogether, this is an excellent resource for instructors and graduate students, as well as an outstanding reference for researchers and practicing engineers.

Foundations of MIMO Communication

Detecting objects in aerial images is challenging for at least two reasons: (1) target objects like pedestrians are very small in pixels, making them hardly distinguished from surrounding background; and (2) targets are in general sparsely and non-uniformly distributed, making the detection very inefficient. In this paper, we address both issues inspired by observing that these targets are often clustered. In particular, we propose a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework. The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet). Given an input image, CPNet produces object cluster regions and ScaleNet estimates object scales for these regions. Then, each scale-normalized cluster region is fed into DetecNet for object detection. ClusDet has several advantages over previous solutions: (1) it greatly reduces the number of chips for final object detection and hence achieves high running time efficiency, (2) the cluster-based scale estimation is more accurate than previously used single-object based ones, hence effectively improves the detection for small objects, and (3) the final DetecNet is dedicated for clustered regions and implicitly models the prior context information so as to boost detection accuracy. The proposed method is tested on three popular aerial image datasets including VisDrone, UAVDT and DOTA. In all experiments, ClusDet achieves promising performance in comparison with state-of-the-art detectors.

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Clustered Object Detection in Aerial Images

Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop In this paper, we investigate the image cropping strategy to address these challenges Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map As pixel intensity varies, it is able to tell whether a region has objects or not, which in turn provides guidance for cropping images statistically DMNet has three key components: a density map generation module, an image cropping module and an object detector DMNet generates a density map and learns scale information based on density intensities to form cropping regions Extensive experiments show that DMNet achieves state-of-the-art performance on two popular aerial image datasets, ie VisionDrone [30] and UAVDT [4]

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Density Map Guided Object Detection in Aerial Images

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part II: Applications

Energy-efficient bitcoin mining cores have gained significant attention since the energy cost for computing dominates the mining expenses [1]. Ultra-low-voltage (ULV) digital circuits have emerged as an attractive approach to improve the energy-efficiency. However, they demand a large timing margin for the worst-case process, voltage, and temperature (PVT) variations, undermining a significant portion of energy savings. Recent works, including multi-phase latch pipeline [1], tunable replica circuits [2]–[3], in-situ error detection and correction (EDAC) [4]–[6], and dynamic timing enhancement [7], can reduce the pessimistic margin. However, it is not straightforward to adopt those techniques in mining cores due to their deeply-pipelined architecture (up to 128 stages [1]). For example, to adopt EDAC, the deep pipeline requires inserting many bulky error detectors as it has many critical paths. Our experiment with a 0.3V 28-nm mining core shows >18.9% registers need to be replaced with error detectors, considering 6σ local process variation only. Also, multiple stages can have timing errors simultaneously, making an error correction process (e.g., clock gating [5], VDD boosting [6]) complex and costly.

TICA: A 0.3V, Variation-Resilient 64-Stage Deeply-Pipelined Bitcoin Mining Core with Timing Slack Inference and Clock Frequency Adaption

To accelerate the computation of deep neural networks (DNNs), their hardware implementations have been extensively explored. Stochastic computing (SC) is recognized as an efficient computing paradigm that can be adopted to design low-complexity architectures for DNNs. However, most of the reported SC-DNNs suffer from long latency. Recently, a novel SC scheme called wire spreading has been proposed to address the issue of latency, delivering fully parallel SC-DNN architectures. This brief aims to further reduce the hardware complexity of the wire-spread-based SC-DNNs (WSSC-DNNs). By investigating the bit distribution, we first propose a consecutive bit compression (CBC) method without introducing any accuracy loss. Moreover, another method called approximate compression (AC) is presented to reduce the number of wires at the expense of acceptable accuracy loss. These two compression methods are compatible and therefore can be utilized simultaneously. In the experiment for SqueezeNet on CIFAR10, it shows that the proposed methods can save up to 70.6% area cost and 67.0% power consumption.

Efficient Compression Methods for Wire-Spread-Based Stochastic Computing Deep Neural Networks

Conventional physics-based memristor device modeling methods highly rely on human expertise, which results in a long development period. To address the aforementioned challenges, we propose a new generalized memristor (GEM) device modeling framework based on the artificial neural network (ANN) technique, which has a minimum dependency on the underlying physics, resulting in a fast turn-around development time for customized memristor devices. GEM framework models the switching and conducting behaviors of the memristor devices separately, avoiding the signal-dependence issue in the prior time-series data modeling method. The result of the GEM framework is a compact model that supports general-purpose circuit simulators. Experimental results show that our compact model achieves a ratio of root-mean-square error to peak-to-peak (RMSE/PP) of 3.6% compared to the physics-based device model. Performance analysis of memristor-based logic and memristor crossbar circuits are conducted to demonstrate the effectiveness of our proposed GEM framework for the design and analysis of memristor-based circuits.

GEM: A Generalized Memristor Device Modeling Framework Based on Neural Network for Transient Circuit Simulation

Inter-chiplet communication requires more and more bandwidth within a limited space, new signaling can be applied to meet this demand. In this paper, a channel model of multi-wires is presented based on UCIe specification. CNRZ-5 is explored as an alternative to PAM-4 for signaling the channels. Simulations show that CNRZ-5 can achieve a 31.17% increment in eye height under the same bit rate and a 13.72% increment in bandwidth density under the minimum COM condition.

Study of Chord Signaling for High-Bandwidth Inter-Chiplet Communication

Time difference amplifier (TDA) is often used in time domain interconnection, computing and measurement. Gain and linearity control are two main design issues. To reduce the nonlinear distortion, a novel self-adaptive pulse shrink circuit is proposed for the SR-latch based time difference amplifier. The multi-stage self-adaptive pulse shrink unit can compensate for the gain error caused by the high-order items and decrease the linearity problem. A programmable gain control circuit is also proposed to improve the gain range of the TDA. The proposed TDA including gain reconfiguration and linearity control is implemented by using SMIC 40nm CMOS technology. Post layout simulation demonstrates that the proposed TDA achieves from 8 to 100 s/s programmable gain within the input linear range $$[-36\,\hbox {ps},36\,\hbox {ps}]$$
 . The total area is $$21.186\,\upmu \hbox {m} \times 18.435\,\upmu \hbox {m}$$
 . The total power consumption is $$31.45\,\upmu \hbox {W}$$
 when the gain is 10 and the toggle frequency is 100 MHz.

Guanghui He

Papers

TICA: A 0.3V, Variation-Resilient 64-Stage Deeply-Pipelined Bitcoin Mining Core with Timing Slack Inference and Clock Frequency Adaption

Efficient Compression Methods for Wire-Spread-Based Stochastic Computing Deep Neural Networks

GEM: A Generalized Memristor Device Modeling Framework Based on Neural Network for Transient Circuit Simulation

Study of Chord Signaling for High-Bandwidth Inter-Chiplet Communication

A gain reconfigurable time difference amplifier with self-adaptive linearity control