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Rakesh Kumar

Publications and source records attributed to Rakesh Kumar.

At least 55 records · Page 3Linked to original sources

Freeway to Memory Level Parallelism in Slice-Out-of-Order Cores

Exploiting memory level parallelism (MLP) is crucial to hide long memory and last level cache access latencies. While out-of-order (OoO) cores, and techniques building on them, are effective at exploiting MLP, they deliver poor energy efficiency due to their complex and energy-hungry hardware. This work revisits slice-out-of-order (sOoO) cores as an energy efficient alternative for MLP exploitation. sOoO cores achieve energy efficiency by constructing and executing \textit{slices} of MLP generating instructions out-of-order only with respect to the rest of instructions; the slices and the remaining instructions, by themselves, execute in-order. However, we observe that existing sOoO cores miss significant MLP opportunities due to their dependence-oblivious in-order slice execution, which causes dependent slices to frequently block MLP generation. To boost MLP generation, we introduce Freeway, a sOoO core based on a new dependence-aware slice execution policy that tracks dependent slices and keeps them from blocking subsequent independent slices and MLP extraction. The proposed core incurs minimal area and power overheads, yet approaches the MLP benefits of fully OoO cores. Our evaluation shows that Freeway delivers 12% better performance than the state-of-the-art sOoO core and is within 7% of the MLP limits of full OoO execution.

cs.AR↗

Almost Yamabe Solitons on a Total Space of Almost Hermitian Submersions

This article presents the study of almost Hermitian submersion from an almost Yamabe soliton onto an almost Hermitian manifold. A non-trivial example is also mentioned in order to guarantee the existence of such solitons on the total space of almost Hermitian submersions. We mainly focus on Kaehler submersions from Kaehler manifolds which are special case of almost Hermitian submersions. Under certain conditions, we find out that the fibres and the base manifold of such submersions are almost Yamabe soliton. We give the characterizations for an almost Yamabe soliton of a Kaehler submersion to be shrinking, steady and expanding in terms of extrinsic horizontal scalar curvature. Moreover, we observe the behavior of torqued, recurrent and concurrent vector field of the total space of the Kaehler submersion. In particular, we obtain characterization for an almost Yamabe soliton consisting of concurrent vector fields. Meanwhile, we give some results of such submersions when the total space is Yamabe soliton which is a particular case of almost Yamabe soliton.

math.DG↗

SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments

This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments. Existing end-to-end learning-based VLN methods struggle at this task as they focus mostly on utilizing raw visual observations and lack the semantic spatio-temporal reasoning capabilities which is crucial in generalizing to new environments. In this regard, we present a hybrid transformer-recurrence model which focuses on combining classical semantic mapping techniques with a learning-based method. Our method creates a temporal semantic memory by building a top-down local ego-centric semantic map and performs cross-modal grounding to align map and language modalities to enable effective learning of VLN policy. Empirical results in a photo-realistic long-horizon simulation environment show that the proposed approach outperforms a variety of state-of-the-art methods and baselines with over 22% relative improvement in SPL in prior unseen environments.

cs.RO↗

ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients

Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. Considering that such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, creating lightweight deep neural networks (DNNs) for embedded devices is crucial. None of the current approximation techniques for object classification DNNs can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, or requirements from the user. In this paper, we introduce ApproxNet, a video object classification system for embedded or mobile clients. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model, rather than creating and maintaining an ensemble of models (e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at runtime to these changes, provides low and stable latency for the image and video frame classification problems, and show the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17], NestDNN [MobiCom-18], and MSDNet [ICLR-18].

cs.CV↗

MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation

Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning-based methods, such as deep reinforcement learning, have the potential to outperform the classical solutions developed for this task; however, they come at a significantly increased computational load. Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget. To this end, we propose a method to encode vital scene semantics such as traversable paths, unexplored areas, and observed scene objects -- alongside raw visual streams such as RGB, depth, and semantic segmentation masks -- into a semantically informed, top-down egocentric map representation. Further, to enable the effective use of this information, we introduce a novel 2-D map attention mechanism, based on the successful multi-layer Transformer networks. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach. We show that by using our novel attention schema and auxiliary rewards to better utilize scene semantics, we outperform multiple baselines trained with only raw inputs or implicit semantic information while operating with an 80% decrease in the agent's experience.

cs.CV↗

A Variable Vector Length SIMD Architecture for HW/SW Co-designed Processors

Hardware/Software (HW/SW) co-designed processors provide a promising solution to the power and complexity problems of the modern microprocessors by keeping their hardware simple. Moreover, they employ several runtime optimizations to improve the performance. One of the most potent optimizations, vectorization, has been utilized by modern microprocessors, to exploit the data level parallelism through SIMD accelerators. Due to their hardware simplicity, these accelerators have evolved in terms of width from 64-bit vectors in Intel MMX to 512-bit wide vector units in Intel Xeon Phi and AVX-512. Although SIMD accelerators are simple in terms of hardware design, code generation for them has always been a challenge. Moreover, increasing vector lengths with each new generation add to this complexity. This paper explores the scalability of SIMD accelerators from the code generation point of view. We discover that the SIMD accelerators remain underutilized at higher vector lengths mainly due to: a) reduced dynamic instruction stream coverage for vectorization and b) increase in permutations. Both of these factors can be attributed to the rigidness of the SIMD architecture. We propose a novel SIMD architecture that possesses the flexibility needed to support higher vector lengths. Furthermore, we propose Variable Length Vectorization and Selective Writing in a HW/SW co-designed environment to transparently target the flexibility of the proposed architecture. We evaluate our proposals using a set of SPECFP2006 and Physicsbench applications. Our experimental results show an average dynamic instruction reduction of 31% and 40% and an average speed up of 13% and 10% for SPECFP2006 and Physicsbench respectively, for 512-bit vector length, over the scalar baseline code.

cs.AR↗

Efficient Kernel based Matched Filter Approach for Segmentation of Retinal Blood Vessels

Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been given for segmentation of retinal vessels but their kernels are not appropriate to vessel profile cause poor performance. To overcome this, a new and efficient kernel based matched filter approach has been proposed. The new matched filter is used to generate the matched filter response (MFR) image. We have applied Otsu thresholding method on obtained MFR image to extract the vessels. We have conducted extensive experiments to choose best value of parameters for the proposed matched filter kernel. The proposed approach has examined and validated on two online available DRIVE and STARE datasets. The proposed approach has specificity 98.50%, 98.23% and accuracy 95.77 %, 95.13% for DRIVE and STARE dataset respectively. Obtained results confirm that the proposed method has better performance than others. The reason behind increased performance is due to appropriate proposed kernel which matches retinal blood vessel profile more accurately.

eess.IV↗

RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization

We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud (rendered as depth images). Prior works were demonstrated on small datasets and did not lend themselves to scaling up for large-scale applications. To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images. We propose a novel joint embedding based method that effectively combines the appearance and semantic cues from both modalities to handle drastic cross-modal variations. Experiments on the proposed dataset show that our model achieves a strong result of a median rank of 5 in matching across a large test set of 50K location pairs collected from a 14km^2 area. This represents a significant advancement over prior works in performance and scale. We conclude with qualitative results to highlight the challenging nature of this task and the benefits of the proposed model. Our work provides a foundation for further research in cross-modal visual localization.

cs.CV↗

Generic Riemannian submersions from nearly Kaehler manifolds

We study generic Riemannian submersions from nearly Kaehler manifolds onto Riemannian manifolds. We investigate conditions for the integrability of various distributions arising for generic Riemannian submersions and also obtain conditions for leaves to be totally geodesic foliations. We obtain conditions for a generic Riemannian submersion to be a totally geodesic map and also study generic Riemannian submersions with totally umbilical fibers. Finally, we derive conditions for generic Riemannian submersions to be harmonic map.

math.DG↗

Constraint preserving discontinuous Galerkin method for ideal compressible MHD on 2-D Cartesian grids

We propose a constraint preserving discontinuous Galerkin method for ideal compressible MHD in two dimensions and using Cartesian grids, which automatically maintains the global divergence-free property. The approximation of the magnetic field is achieved using Raviart-Thomas polynomials and the DG scheme is based on evolving certain moments of these polynomials which automatically guarantees divergence-free property. We also develop HLL-type multi-dimensional Riemann solvers to estimate the electric field at vertices which are consistent with the 1-D Riemann solvers. When limiters are used, the divergence-free property may be lost and it is recovered by a divergence-free reconstruction step. We show the performance of the method on a range of test cases up to fourth order of accuracy.

math.NA↗

Fetch-Directed Instruction Prefetching Revisited

Prior work has observed that fetch-directed prefetching (FDIP) is highly effective at covering instruction cache misses. The key to FDIP's effectiveness is having a sufficiently large BTB to accommodate the application's branch working set. In this work, we introduce several optimizations that significantly extend the reach of the BTB within the available storage budget. Our optimizations target nearly every source of storage overhead in each BTB entry; namely, the tag, target address, and size fields. We observe that while most dynamic branch instances have short offsets, a large number of branches has longer offsets or requires the use of full target addresses. Based on this insight, we break-up the BTB into multiple smaller BTBs, each storing offsets of different length. This enables a dramatic reduction in storage for target addresses. We further compress tags to 16 bits and avoid the use of the basic-block-oriented BTB advocated in prior FDIP variants. The latter optimization eliminates the need to store the basic block size in each BTB entry. Our final design, called FDIP-X, uses an ensemble of 4 BTBs and always outperforms conventional FDIP with a unified basic-block-oriented BTB for equal storage budgets.

cs.AR↗

Scrutiny of stagnation region flow in a nanofluid suspended permeable medium due to inconsistent heat source/sink

In present analysis, nanofluid transport near to a stagnation region over a bidirectionally deforming surface is scrutinized. The region is embedded with Darcy-Forchheimer medium which supports permeability. The porous matrix is suspended with nanofluid, and surface is under the influence of inconsistent heat source/sink. Using similarity functions, framed governing equations are switched to a collection of ordinary differential equations. Output is procured via optimal homotopy asymptotic method (OHAM). Basic notion of OHAM for a vector differential set-up is presented along with required convergence theorems. At different flow stagnation strengths, nanofluid behavior is investigated with respect to variations in porosity parameter, Forchheimer number, Brownian motion, stretching ratio, thermophoretic force, heat source/sink and Schimdt number. Stagnation flow strength invert the pattern of boundary layer profiles of primary velocity. Heat transfer has straightforward relation with Forchheimer number when stagnation forces dominate stretching forces

physics.flu-dyn↗

Signatures of non-trivial band topology in LaAs/LaBi heterostructure

In this article, we investigate non-trivial topological features in a heterostructure of extreme magnetoresistance (XMR) materials LaAs and LaBi using density functional theory (DFT). The proposed heterostructure is found to be dynamically stable and shows bulk band inversion with non-trivial Z_{2} topological invariant and a Dirac cone at the surface. In addition, its electron and hole carrier densities ratio is also calculated to investigate the possibility to possess XMR effect. Electrons and holes in the heterostructure are found to be nearly compensated, thereby facilitating it to be a suitable candidate for XMR studies.

cond-mat.mtrl-sci↗

Studies of non-trivial band topology and electron-hole compensation in YSb

In this article, we study non-trivial topological phase and electron-hole compensation in extremely large magnetoresistance (XMR) material YSb under hydrostatic pressure using first-principles calculations. YSb is topologically trivial at ambient pressure, but undergoes a reentrant topological phase transition under hydrostatic pressure. The reentrant behavior of topological quantum phase is then studied as a function of charge density ratio under pressure. From the detailed investigation of Fermi surfaces, it is found that electron to hole densities ratio increases with pressure, however a non-trivial topological phase appears without perfect electron-hole compensation. The results indicate that the non-trivial topological phase under hydrostatic pressure may not have maximal influence on the magnetoresistance, and need further investigations through experiments to determine the exact relationship between topology and XMR effect.

cond-mat.mtrl-sci↗

New wavelet method based on Shifted Lucas polynomials: A tau approach

In current work, non-familiar shifted Lucas polynomials are introduced. We have constructed a computational wavelet technique for solution of initial/boundary value second order differential equations. For this numerical scheme, we have developed weight function and Rodrigues' formula for Lucas polynomials. Further, Lucas polynomials and their properties are used to propose shifted Lucas polynomials and then utilization of shifted Lucas polynomials provides us shifted Lucas wavelet. We furnished the operational matrix of differentiation and the product operational matrix of the shifted Lucas wavelets. Moreover, convergence and error analysis ensure accuracy of the proposed method. Illustrative examples show that the present method is numerically fruitful, effective and convenient for solving differential equations

math.NA↗

Positivity-preserving finite difference WENO scheme for Ten-Moment equations with source term

We develop a positivity-preserving finite difference WENO scheme for the Ten-Moment equations with body forces acting as a source in the momentum and energy equations. A positive forward Euler scheme under a CFL condition is first constructed which is combined with an operator splitting approach together with an integrating factor, strong stability preserving Runge-Kutta scheme. The positivity of the forward Euler scheme is obtained under a CFL condition by using a scaling type limiter, while the solution of the source operator is performed exactly and is positive without any restriction on the time step. The proposed method can be used with any WENO reconstruction scheme and we demonstrate it with fifth order accurate WENO-JS, WENO-Z and WENO-AO schemes. An adaptive CFL strategy is developed which can be more efficient than the use of reduced CFL for positivity preservation. Numerical results show that high order accuracy and positivity preservation are achieved on a range of test problems.

math.NA↗

Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval

While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA). In our proposed ExAG, a human user needs to guess a secret image picked by the VQA agent by asking natural language questions to it. We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as "helpful" significantly improve human performance, "incorrect" and "unhelpful" explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.

cs.CY↗

Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization

We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags. Compared to appearance features, the semantic layout of a scene is generally more invariant to appearance variations. We use this intuition and propose a novel end-to-end deep attention-based framework that utilizes multimodal cues to generate robust embeddings for 2D-VL. The proposed attention module predicts a shared channel attention and modality-specific spatial attentions to guide the embeddings to focus on more reliable image regions. We evaluate our model against state-of-the-art (SOTA) methods on three challenging localization datasets. We report an average (absolute) improvement of $19\%$ over current SOTA for 2D-VL. Furthermore, we present an extensive study demonstrating the contribution of each component of our model, showing $8$--$15\%$ and $4\%$ improvement from adding semantic information and our proposed attention module. We finally show the predicted attention maps to offer useful insights into our model.

cs.CV↗