Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-layer Design for Computing-in-Memory: From Devices, Circuits, to Architectures and Applications

The era of Big Data, Artificial Intelligence (AI) and Internet of Things (IoT) is approaching, but our underlying computing infrastructures are not sufficiently ready. The end of Moore’s law and process scaling as well as the memory wall associated with von Neumann architectures have throttled the rapid development of conventional architectures based on CMOS technology, and cross-layer efforts that involve the interactions from low-end devices to high-end applications have been prominently studied to overcome the aforementioned challenges. On one hand, various emerging devices, e.g., Ferroelectric FET, have been proposed to either sustain the scaling trends or enable novel circuit and architecture innovations. On the other hand, novel computing architectures/algorithms, e.g., computing-in-memory (CiM), have been proposed to address the challenges faced by conventional von Neumann architectures. Naturally, integrated approaches across the emerging devices and computing architectures/algorithms for data-intensive applications are of great interests. This paper uses the FeFET as a representative device, and discuss about the challenges, opportunities and contributions for the emerging trends of cross-layer co-design for CiM.
Nondestructive, high-resolution, chemically specific 3D nanostructure characterization using phase-sensitive EUV imaging reflectometry

A new imaging technique enables nondestructive, 3D characterization of nanostructures, their composition, and interfaces. Next-generation nano- and quantum devices have increasingly complex 3D structure. As the dimensions of these devices shrink to the nanoscale, their performance is often governed by interface quality or precise chemical or dopant composition. Here, we present the first phase-sensitive extreme ultraviolet imaging reflectometer. It combines the excellent phase stability of coherent high-harmonic sources, the unique chemical sensitivity of extreme ultraviolet reflectometry, and state-of-the-art ptychography imaging algorithms. This tabletop microscope can nondestructively probe surface topography, layer thicknesses, and interface quality, as well as dopant concentrations and profiles. High-fidelity imaging was achieved by implementing variable-angle ptychographic imaging, by using total variation regularization to mitigate noise and artifacts in the reconstructed image, and by using a high-brightness, high-harmonic source with excellent intensity and wavefront stability. We validate our measurements through multiscale, multimodal imaging to show that this technique has unique advantages compared with other techniques based on electron and scanning probe microscopies.
A Dynamic Link-latency Aware Cache Replacement Policy (DLRP)

Multiprocessor system-on-chips (MPSoCs) in modern devices have mostly adopted the non-uniform cache architecture (NUCA) [1], which features varied physical distance from cores to data locations and, as a result, varied access latency. In the past, researchers focused on minimizing the average access latency of the NUCA. We found that dynamic latency is also a critical index of the performance. A cache access pattern with long dynamic latency will result in a significant cache performance degradation without considering dynamic latency. We have also observed that a set of commonly used neural network application kernels, including the neural network fully-connected and convolutional layers, contains substantial accessing patterns with long dynamic latency. This paper proposes a hardware-friendly dynamic latency identification mechanism to detect such patterns and a dynamic link-latency aware replacement policy (DLRP) to improve cache performance based on the NUCA.The proposed DLRP, on average, outperforms the least recently used (LRU) policy by 53% with little hardware overhead. Moreover, on average, our method achieves 45% and 24% more performance improvement than the not recently used (NRU) policy and the static re-reference interval prediction (SRRIP) policy normalized to LRU.
RIME: A Scalable and Energy-Efficient Processing-In-Memory Architecture for Floating-Point Operations

Processing in-memory (PIM) is an emerging technology poised to break the memory-wall in the conventional von Neumann architecture. PIM reduces data movement from the memory systems to the CPU by utilizing memory cells for logic computation. However, existing PIM designs do not support high precision computation (e.g., floating-point operations) essential for critical data-intensive applications. Furthermore, PIM architectures require complex control module and costly peripheral circuits to harness the full potential of in-memory computation. These peripherals and control modules usually suffer from scalability and efficiency issues.Hence, in this paper, we explore the analog properties of the resistive random access memory (RRAM) crossbar and propose a scalable RRAM-based in-memory floating-point computation architeture (RIME). RIME uses single-cycle NOR, NAND, and Minority logic to achieve floating-point operations. RIME features a centralized control module and a simplified peripheral circuit to eliminate data movement during parallel computation. An experimental 32-bit RIME multiplier demonstrates 4.8X speedup, 1.9X area-improvement, and 5.4X energy-efficiency than state-of-the-art RRAM-based PIM multipliers.
Threat Modeling based Penetration Testing: The Open Energy Monitor Case study

Currently, the widespread diffusion of intelligent objects connected to the Internet and continuously interacting with people is a fact. However, such a paradigm has a side effect in terms of privacy and security: personal data and the control of critical devices (eg. boiler, air conditioning, video surveillance, controlled gates, .) are often demanded to home automation systems, often managed by non-expert users and, consequently, likely exposed to multiple security threats. This article follows a research line that aims to offer a systematic way to identify threats in the Internet of Things systems, and consequently plan penetration testing procedures, automated as much as possible, that outline possible security holes and help to gain awareness on the issues related to this new technologies. In this paper, we addressed a typical home system, the Open Energy Monitor, to demonstrate our methodology. In this analysis we focus on the MQTT protocol, commonly used for communication between IoT devices, proposing a complete Threat Model for this protocol. The main innovative contribution of this paper relates to the catalog of threats made available for MQTT-based devices (highly reusable in different environments) and on the planning of penetration tests, that relies on the adoption of a cyber threat intelligence database that collects common attack patterns, offered by MITRE.
Residue-Net: Multiplication-free Neural Network by In-situ No-loss Migration to Residue Number Systems

Deep eural networks are widely deployed on embedded devices to solve a wide range of problems from edge-sensing to autonomous driving. The accuracy of these networks is usually proportional to their complexity. Quantization of model parameters (i.e., weights) and/or activations to alleviate the complexity of these networks while preserving accuracy is a popular powerful technique. Nonetheless, previous studies have shown that quantization level is limited as the accuracy of the network decreases afterward. We propose Residue-Net, a multiplication-free accelerator for neural networks that uses Residue Number System (RNS) to achieve substantial energy reduction. RNS breaks down the operations to several smaller operations that are simpler to implement. Moreover, Residue-Net replaces the copious of costly multiplications with non-complex, energy-efficient shift and add operations to further simplify the computational complexity of neural networks. To evaluate the efficiency of our proposed accelerator, we compared the performance of Residue-Net with a baseline FPGA implementation of four widely-used networks, viz., LeNet, AlexNet, VGG16, and ResNet-50. When delivering the same performance as the baseline, Residue-Net reduces the area and power (hence energy) respectively by 36% and 23%, on average with no accuracy loss. Leveraging the saved area to accelerate the quantized RNS network through parallelism, Residue-Net improves its throughput by 2.8× and energy by 2.7×.
A gamification experiment of the marczewski's typology test

The Marczewski's Typology differs from other types of player profiles by addressing the gamification context. One form of self-evaluation to discover your player profile is through a simple questionnaire. So, in order to explore ways to increase user experience, this paper reports an experiment to develop an alternative test for self-evaluation by this typology. Named a Gamified Test, it is based on RPG genre and was compared to the original test. There was a quantitative and qualitative evaluation performed by computer students that showed differences and similarities between them.
A Quantized Training Framework for Robust and Accurate ReRAM-based Neural Network Accelerators

Neural networks (NN), especially deep neural networks (DNN), have achieved great success in lots of fields. ReRAM crossbar, as a promising candidate, is widely employed to accelerate neural network owing to its nature of processing MVM. However, ReRAM crossbar suffers high conductance variation due to many non-ideal effects, resulting in great inference accuracy degradation. Recent works use uniform quantization to enhance the tolerance of conductance variation, but these methods still suffer high accuracy loss with large variation. In this paper, firstly, we analyze the impact of the quantization and conductance variation on the accuracy. Then, based on two observation, we propose a quantized training framework to enhance the robustness and accuracy of the neural network running on the accelerator, by introducing a smart non-uniform quantizer. This framework consists of a robust trainable quantizer and a corresponding training method, and needs no extra hardware overhead and compatible with a standard neural network training procedure. Experimental results show that our proposed method can improve inference accuracy by 10% ∼ 30% under large variation, compared with uniform quantization method.
A Comparative Study of ML-ELM and DNN for Intrusion Detection

Intrusion detection remains one of the critical research issues in network security. Many machine learning algorithms have been proposed to develop intrusion detection systems, which can categorize network traffic into normal and anomalous classes. The multilayer extreme learning machine (ML-ELM) and the deep neural network (DNN) are two machine learning algorithms based on different theories/concepts that use the same multilayer architecture. In this paper, a comparative study is performed to shed light on the selection between these two algorithms with the same architecture in intrusion detection applications. The study explores the performance of the ML-ELM and DNN algorithms under similar parameter settings. With in-depth analysis and discussions, the limitations and advantages of each algorithm are outlined. In addition, the performance trend of each algorithm with increasing parameter values is studied.
CyberSecurity Attack Prediction: A Deep Learning Approach

Cybersecurity attacks are exponentially increasing, making existing detection mechanisms insufficient and enhancing the necessity to design more relevant prediction models and approaches. This issue is still an open research problem since existing attack prediction models are failing to follow the huge amount of attacks and their variety. Recently, machine learning approaches and especially deep learning techniques have received much attention from researchers since their unparalleled high performance in several prediction-based fields. In this context, this paper explores the application of deep learning techniques for predicting cybersecurity attacks. Particularly, it proposes a new LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and MLP (Multilayer Perceptron) based models carefully designed to predict the type of attack potentially to hap-pen. The proposed models were validated using a recently available dataset called CTF showing encouraging results especially for the LSTM model with an f-measure greater than 93%.
Univariate Forecasting of time series data Based on Seq2Seq Model

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction.
Kreon

Persistent key-value stores have emerged as a main component in the data access path of modern data processing systems. However, they exhibit high CPU and I/O overhead. Nowadays, due to power limitations, it is important to reduce CPU overheads for data processing. In this article, we propose Kreon, a key-value store that targets servers with flash-based storage, where CPU overhead and I/O amplification are more significant bottlenecks compared to I/O randomness. We first observe that two significant sources of overhead in key-value stores are: (a) The use of compaction in Log-Structured Merge-Trees (LSM-Tree) that constantly perform merging and sorting of large data segments and (b) the use of an I/O cache to access devices, which incurs overhead even for data that reside in memory. To avoid these, Kreon performs data movement from level to level by using partial reorganization instead of full data reorganization via the use of a full index per-level. Kreon uses memory-mapped I/O via a custom kernel path to avoid a user-space cache. For a large dataset, Kreon reduces CPU cycles/op by up to 5.8×, reduces I/O amplification for inserts by up to 4.61×, and increases insert ops/s by up to 5.3×, compared to RocksDB.
Energy-Performance Co-Management of Mixed-Sensitivity Workloads on Heterogeneous Multi-core Systems

Satisfying performance of complex workload scenarios with respect to energy consumption on Heterogeneous Multicore Platforms (HMPs) is challenging when considering i) the increasing variety of applications, and ii) the large space of resource management configurations. Existing run-time resource management approaches use online and oﬄine learning to handle such complexity. However, they focus on one type of application, neglecting concurrent execution of mixed sensitivity workloads. In this work, we propose an energy-performance co-management method which prioritizes mixed type of applications at run-time, and searches in the configuration space to find the optimal configuration for each application which satisfies the performance requirements while saving energy. We evaluate our approach on a real Odroid XU3 platform over mixed-sensitivity embedded workloads. Experimental results show our approach provides 54% lower performance violation with 50% higher energy saving compared to the existing approaches.
Alzheimer's Disease Classification Using Structural MRI Based on Convolutional Neural Networks

Research on Alzheimer's Disease auxiliary diagnosis based on deep learning has been receiving more and more attention. However, in existing AD classification research based on T1-weighted structural Magnetic Resonance Imaging, the influence of different MRI scanning modes (i.e., 1.5T and 3T) of ADNI database is not well considered. In addition, there are few attempts to use the Voxel-Based Morphometry-Diffeomorphic Anatomical Registration Through Exponential Lie algebra method for data preprocessing in AD classification experiments. Therefore, the VBM-DARTEL method is used to preprocess the sMRI data. Then, based on different scanning methods, the preprocessed ADNI subsets are further divided according to three data partitioning methods (subject, MRI and visit-history), on which five representative convolutional neural networks are adopted for classification and evaluation. The experimental results demonstrate that the overall classification performance on both 1.5T and 3T datasets is visit-history>MRI>subject. Meanwhile, the best accuracy rates 95.87% and 97.15% are achieved by GoogLeNet on 1.5T and 3T datasets in visit-history level. In addition, preprocessing operation improves the accuracy rate of AD classification to a certain extent and the performance of the five CNN models is equivalent in preprocessed datasets, while there is obviously accuracy difference on none-preprocessed datasets.
A Convolutional Neural Network-Based Noise Filtering Method for Photon-Counting LiDAR Data

Photon-counting LiDAR plays an important role in the surveying and mapping areas with its advantages of high sensitivity and low emission power. In recent years, LiDAR is used for a variety of scenarios including drones, unmanned vehicles, and even consumer electronics. The photon-counting detection system receives return points on the photon level, which retains rich detail than can be used for analysis. However, it is so sensitive that data are full of noise points. It is important to filter out noise in data preprocessing in order to prevent noise from affecting post-analysis. We proposed a convolutional neural network-based method to solve the noise filtering problem, which accepts varied input sizes and outputs noise labels for each point. Symmetry functions are designed to ensure consistent results on unordered data. Global features are extracted by the shared multilayer perceptron (MLP) and max pooling layer. Local features are extracted from each point and its spatial neighbors through the convolutional operator. We combine global and local features and build the network based on shared MLP to learn pointwise segmentation, which is the label of noise, from these combined features. The performance of our proposed method is evaluated by simulated data and MABEL data. It shows that our method can effectively eliminate most of noise points from data of photon-counting LiDAR.
Tour de Tune 2 - Auditory-Game-Motor Synchronisation with Music Tempo in an Immersive Virtual Reality Exergame

Physical activity has numerous benefits, but a large proportion of the population do not exercise enough. Exergaming, the combination of exercises and gaming, has been suggested as a means to increase physical activity for people not intrinsically motivated to exercise. Despite promising results, usage of exergames outside research projects is limited and commercial exercise games usually do not promote a sufficient exercise intensity. Music has been shown to be able to increase motivation and performance, and reduce the perceived level of exertion during exercises. In this research we investigate whether users’ perception of exercises can be improved by synchronising music and gameplay. We evaluate the subjective experience of our system using a within-subject comparison between four different conditions: i) control condition with no music or gameplay, ii) gameplay-only condition with no music, iii) music-gameplay condition with asynchronous music, and iv) music-gameplay condition with synchronous music. Our results show that both of the music-gameplay conditions resulted in a significant increase in motivation and perceived value of activity, and a significant decrease in distress, when compared with the gameplay-only and control conditions. We discuss the results and their practical implications, and make suggestions for future research.
A Single ZnO Nanofiber-Based Highly Sensitive Amperometric Glucose Biosensor

A novel fabrication approach of a highly sensitive amperometric glucose biosensor based on a single ZnO nanofiber (ZONF) is presented. Nanofibers (NFs) of poly(vinyl pyrrolidone)/zinc acetate composite have been synthesized by electrospinning technique. By high-temperature calcinations of the above precursor fibers, ZONFs with diameters in the range of 350−195 nm have been successfully obtained. A single NF on a gold electrode is functionalized with glucose oxidase (GOx) by physical adsorption. Electrochemical measurements of the biosensor revealed a high and reproducible sensitivity of 70.2 μA cm−2 mM−1 within a response time of less than 4 s. The biosensor also showed a linear range from 0.25 to 19 mM with a low limit of detection (LOD) of 1 μM. Furthermore, it has been revealed that the biosensor exhibits a good anti-interference ability and favorable stability over relatively long-term storage (more than 4 months). All these results strongly suggest that a single ZONF can provide a new platform for bi.
Complete Genome Sequence of Phascolarctobacterium faecium G 104, Isolated from the Stools of a Healthy Lean Donor

Phascolarctobacterium faecium is a strict anaerobe belonging to the Firmicutes phylum that is found abundantly in the human gastrointestinal tract. Here, we report the complete genome sequence of P. faecium G 104, a strain isolated from a fresh stool sample from a healthy lean donor. ABSTRACT Phascolarctobacterium faecium is a strict anaerobe belonging to the Firmicutes phylum that is found abundantly in the human gastrointestinal tract. Here, we report the complete genome sequence of P. faecium G 104, a strain isolated from a fresh stool sample from a healthy lean donor.
Bridging the Frequency Gap in Heterogeneous 3D SoCs through Technology-Specific NoC Router Architectures

In heterogeneous 3D System-on-Chips (SoCs), NoCs with uniform properties suffer one major limitation; the clock frequency of routers varies due to different manufacturing technologies. For example, digital nodes allow for a higher clock frequency of routers than mixed-signal nodes. This large frequency gap is commonly tackled by complex and expensive pseudo-mesochronous or asynchronous router architectures. Here, a more efficient approach is chosen to bridge the frequency gap. We propose to use a heterogeneous network architecture. We show that reducing the number of VCs allows to bridge a frequency gap of up to 2×. We achieve a system-level latency improvement of up to 47% for uniform random traffic and up to 59% for PARSEC benchmarks, a maximum throughput increase of 50%, up to 68% reduced area and 38% reduced power in an exemplary setting combining 15-nm digital and 30-nm mixed-signal nodes and comparing against a homogeneous synchronous network architecture. Versus asynchronous and pseudo-mesochronous router architectures, the proposed optimization consistently performs better in area, in power and the average flit latency improvement can be larger than 51%.