Nearest neighbor (NN) search computations are at the core of many applications such as few-shot learning, classification, and hyperdimensional computing. As such, efficient hardware support for NN ...search is highly desired. In-memory computing using emerging devices offers attractive solutions for NN search. Solutions based on ternary content-addressable memories (TCAMs) offer high energy and latency improvements for NN search at the expense of accuracy. In this work, we propose a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (FeFETs) to perform a single-step, in-memory NN search. We evaluate the efficacy of FeFET MCAMs in the context of few-shot learning applications with different datasets. As an example, we achieve a 78.54% accuracy for a 5-way, 5-shot classification task for the mini-ImageNet dataset (only 1.5% lower than software-based implementations) when using a 3-bit MCAM for NN search. We consider the effects of FeFET threshold voltage variations on the application accuracy and analyze the area and search energy requirements of FeFET MCAMs for accurate operations. Our results indicate that MCAMs require 2× lower area and search energy than TCAMs to achieve the same accuracy. Furthermore, we experimentally demonstrate a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate the design concept.
Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received relatively little ...attention so far. Based on an in-depth analysis of the key computational patterns in state-of-the-art gradient-based training methods, we propose an efficient near-memory acceleration engine called NTX that can be used to train state-of-the-art deep convolutional neural networks at scale. Our main contributions are: (i) a loose coupling of RISC-V cores and NTX co-processors reducing offloading overhead by <inline-formula><tex-math notation="LaTeX">7\times</tex-math> <mml:math><mml:mrow><mml:mn>7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="schuiki-ieq1-2876312.gif"/> </inline-formula> over previously published results; (ii) an optimized IEEE 754 compliant data path for fast high-precision convolutions and gradient propagation; (iii) evaluation of near-memory computing with NTX embedded into residual area on the Logic Base die of a Hybrid Memory Cube; and (iv) a scaling analysis to meshes of HMCs in a data center scenario. We demonstrate a <inline-formula><tex-math notation="LaTeX">2.7\times</tex-math> <mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:mn>7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="schuiki-ieq2-2876312.gif"/> </inline-formula> energy efficiency improvement of NTX over contemporary GPUs at <inline-formula><tex-math notation="LaTeX">4.4\times</tex-math> <mml:math><mml:mrow><mml:mn>4</mml:mn><mml:mo>.</mml:mo><mml:mn>4</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="schuiki-ieq3-2876312.gif"/> </inline-formula> less silicon area, and a compute performance of 1.2 Tflop/s for training large state-of-the-art networks with full floating-point precision. At the data center scale, a mesh of NTX achieves above 95 percent parallel and energy efficiency, while providing <inline-formula><tex-math notation="LaTeX">2.1\times</tex-math> <mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:mn>1</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="schuiki-ieq4-2876312.gif"/> </inline-formula> energy savings or <inline-formula><tex-math notation="LaTeX">3.1\times</tex-math> <mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:mn>1</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="schuiki-ieq5-2876312.gif"/> </inline-formula> performance improvement over a GPU-based system.
The Last Level Cache (LLC) plays a key role in the system performance of current multi-cores by reducing the number of long latency main memory accesses. The inter-application interference at this ...shared resource, however, can lead the system to undesired situations regarding performance and fairness. Recent approaches have successfully addressed fairness and turnaround time (TT) in commercial processors. Nevertheless, these approaches must face sustaining system performance, which is challenging. This work makes two main contributions. LLC behaviors regarding cache performance, data reuse and cache occupancy, that adversely impact on the final performance are identified. Second, based on these behaviors, we propose the Critical-Phase Aware Partitioning Approach (CPA) , which reduces TT while sustaining (and even improving) IPC by making an effective use of the LLC space. Experimental results show that CPA outperforms CA, Dunn and KPart state-of-the-art approaches, and improves TT (over 40 percent in some workloads) over Linux default behavior while sustaining or even improving IPC by more than 3 percent in several mixes.
The practical relevance of
process mining
is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting ...knowledge from event logs. The two most prominent process mining tasks are: (i)
process discovery
: learning a process model from example behavior recorded in an event log, and (ii)
conformance checking
: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a
generic approach to decompose process mining problems
. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
In the service and manufacturing industry, memory‐type control charts are extensively applied for monitoring the production process. These types of charts have the ability to efficiently detect ...disturbances, especially of smaller amount, in the process mean and/or dispersion. Recently, a new homogeneously weighted moving average (HWMA) chart has been proposed for efficient monitoring of smaller shifts. In this study, we have proposed a new double HWMA (DHWMA) chart to monitor the changes in the process mean. The run length profile of the proposed DHWMA chart is evaluated and compared with some existing control charts. The outcomes reveal that the DHWMA chart shows better performance over its competitor charts. The effect of non‐normality (in terms of robustness) and the estimation of the unknown parameters on the performance of the DHWMA chart are also investigated as a part of this study. Finally, a real‐life industrial application is offered to demonstrate the proposal for practical considerations.
Skin cancer is a commonly occurring disease, which affects people of all age groups. Automated detection of skin cancer is needed to decrease the death rate by identifying the diseases at the initial ...stage. The visual inspection during the medical examination of skin lesions is a tedious process as the resemblance among the lesions exists. Recently, imaging-based Computer Aided Diagnosis (CAD) model is widely used to screen and detect the skin cancer. This paper is designed with automated Deep Learning with a class attention layer based CAD model for skin lesion detection and classification known as DLCAL-SLDC. The goal of the DLCAL-SLDC model is to detect and classify the different types of skin cancer using dermoscopic images. During image pre-processing, Dull razor approach-based hair removal and average median filtering-based noise removal processes take place. Tsallis entropy based segmentation technique is applied to detect the affected lesion areas in the dermoscopic images. Also, a DLCAL based feature extractor is used for extracting the features from the segmented lesions using Capsule Network (CapsNet) along with CAL and Adagrad optimizer. The CAL layer incorporated into the CapsNet is intended to capture the discriminative class-specific features to cover the class dependencies and effectively bridge the CapsNet for further process. Finally, the classification is carried out by the Swallow Swarm Optimization (SSO) algorithm based Convolutional Sparse Autoencoder (CSAE) known as SSO-CSAE model. The proposed DLCAL-SLDC technique is validated using a benchmark ISIC dataset. The proposed framework has accomplished promising results with 98.50% accuracy, 94.5% sensitivity and 99.1% specificity over the other methods interms of different measures.
Knowledge discovery from various perspectives has become a crucial asset in almost all fields. Sentimental analysis is a classification task used to classify the sentence based on the meaning of ...their context. This paper addresses class imbalance problem which is one of the important issues in sentimental analysis. Not much works focused on sentimental analysis with imbalanced class label distribution. The paper also focusses on another aspect of the problem which involves a concept called “Code Mixing”. Code mixed data consists of text alternating between two or more languages. Class imbalance distribution is a commonly noted phenomenon in a code-mixed data. The existing works have focused more on analyzing the sentiments in a monolingual data but not in a code-mixed data. This paper addresses all these issues and comes up with a solution to analyze sentiments for a class imbalanced code-mixed data using sampling technique combined with levenshtein distance metrics. Furthermore, this paper compares the performances of various machine learning approaches namely, Random Forest Classifier, Logistic Regression, XGBoost classifier, Support Vector Machine and Naïve Bayes Classifier using F1- Score.
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