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Your search keyword '"Singh, Gagandeep"' showing total 43 results
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43 results on '"Singh, Gagandeep"'

8. LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning

Author

Singh, Gagandeep, Diamantopoulos, Dionysios, Gómez-Luna, Juan, Stuijk, Sander, Corporaal, Henk, and Mutlu, Onur

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Hardware Architecture (cs.AR), Computer Science - Hardware Architecture, Machine Learning (cs.LG)
Abstract

Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design space exploration. First, training requires large amounts of data (features extracted from design synthesis and implementation tools), which is cost-inefficient because of the time-consuming accelerator design and implementation process. Second, a model trained for a specific environment cannot predict performance or resource usage for a new, unknown environment. In a cloud system, renting a platform for data collection to build an ML model can significantly increase the total-cost-ownership (TCO) of a system. Third, ML-based models trained using a limited number of samples are prone to overfitting. To overcome these limitations, we propose LEAPER, a transfer learning-based approach for prediction of performance and resource usage in FPGA-based systems. The key idea of LEAPER is to transfer an ML-based performance and resource usage model trained for a low-end edge environment to a new, high-end cloud environment to provide fast and accurate predictions for accelerator implementation. Experimental results show that LEAPER (1) provides, on average across six workloads and five FPGAs, 85% accuracy when we use our transferred model for prediction in a cloud environment with 5-shot learning and (2) reduces design-space exploration time for accelerator implementation on an FPGA by 10x, from days to only a few hours.

Published
2022

16. Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma.

Author

Ismail, Marwa, Prasanna, Prateek, Bera, Kaustav, Statsevych, Volodymyr, Hill, Virginia, Singh, Gagandeep, Partovi, Sasan, Beig, Niha, McGarry, Sean, Laviolette, Peter, Ahluwalia, Manmeet, Madabhushi, Anant, and Tiwari, Pallavi

Subjects
INDUCTIVE effect, GLIOBLASTOMA multiforme, BRAIN tumors, HETEROGENEITY, PROGNOSIS
Abstract

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients’ MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 (${n}{1} = {53}$), Cohort 2 (${n}{2} = {75}$), and Cohort 3 (${n}{3} = {79}$)), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 − 19) and 5 (3 − 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 − 2) and 3 (2 − 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 − 57) and 12 (6 − 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors. [ABSTRACT FROM AUTHOR]

Published
2022
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17. NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling

Author

Singh, Gagandeep, Diamantopoulos, Dionysios, Hagleitner, Christoph, Gómez-Luna, Juan, Stuijk, Sander, Mutlu, Onur, Corporaal, Henk, Mentens, Nele, Sousa, Leonel, Trancoso, Pedro, Pericas, Miquel, Sourdis, Ioannis, Electronic Systems, Efficient Stream Processing Lab, Center for Care & Cure Technology Eindhoven, EAISI High Tech Systems, and EAISI Foundational

Subjects
FOS: Computer and information sciences, 010302 applied physics, Computer science, SDG 13 – Klimaatactie, Weather and climate, 02 engineering and technology, Energy consumption, High Bandwidth Memory, FLOPS, 01 natural sciences, 7. Clean energy, Stencil, 020202 computer hardware & architecture, Computational science, Acceleration, 13. Climate action, Hardware Architecture (cs.AR), 0103 physical sciences, SDG 13 - Climate Action, 0202 electrical engineering, electronic engineering, information engineering, SDG 7 - Affordable and Clean Energy, Computer Science - Hardware Architecture, Field-programmable gate array, SDG 7 – Betaalbare en schone energie, Efficient energy use
Abstract

Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU implementations suffer from limited performance and high energy consumption. These implementations are dominated by complex irregular memory access patterns and low arithmetic intensity that pose fundamental challenges to acceleration. To overcome these challenges, we propose and evaluate the use of near-memory acceleration using a reconfigurable fabric with high-bandwidth memory (HBM). We focus on compound stencils that are fundamental kernels in weather prediction models. By using high-level synthesis techniques, we develop NERO, an FPGA+HBM-based accelerator connected through IBM CAPI2 (Coherent Accelerator Processor Interface) to an IBM POWER9 host system. Our experimental results show that NERO outperforms a 16-core POWER9 system by 4.2x and 8.3x when running two different compound stencil kernels. NERO reduces the energy consumption by 22x and 29x for the same two kernels over the POWER9 system with an energy efficiency of 1.5 GFLOPS/Watt and 17.3 GFLOPS/Watt. We conclude that employing near-memory acceleration solutions for weather prediction modeling is promising as a means to achieve both high performance and high energy efficiency., This paper appears in FPL 2020

Published
2020

23. FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications.

Author

Singh, Gagandeep, Alser, Mohammed, Cali, Damla Senol, Diamantopoulos, Dionysios, Gomez-Luna, Juan, Corporaal, Henk, and Mutlu, Onur

Subjects
*WEATHER forecasting, *GATE array circuits, *FIELD programmable gate arrays, *SUPPLY & demand, *COMPUTER systems, *BOTTLENECKS (Manufacturing)
Abstract

Modern data-intensive applications demand high computational capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both execution cycles and energy in current computing systems due to the costly data movement between the computation units and the memory units. Genome analysis and weather prediction are two examples of such applications. Recent field-programmable gate arrays (FPGAs) couple a reconfigurable fabric with high-bandwidth memory (HBM) to enable more efficient data movement and improve overall performance and energy efficiency. This trend is an example of a paradigm shift to near-memory computing. We leverage such an FPGA with HBM for improving the prealignment filtering step of genome analysis and representative kernels from a weather prediction model. Our evaluation demonstrates large speedups and energy savings over a high-end IBM POWER9 system and a conventional FPGA board with DDR4 memory. We conclude that FPGA-based near-memory computing has the potential to alleviate the data movement bottleneck for modern data-intensive applications. [ABSTRACT FROM AUTHOR]

Published
2021
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42. Novel Relay Selection Rules for Average Interference-Constrained Cognitive AF Relay Networks.

Author

Das, Priyanka, Mehta, Neelesh B., and Singh, Gagandeep

Abstract

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio (CR) network. However, unlike conventional relaying, the state of the links between the relay and the primary receiver affects the choice of the relay. Further, while the optimal amplify-and-forward (AF) relay selection rule for underlay CR is well understood for the peak interference-constraint, this is not so for the less conservative average interference constraint. For the latter, we present three novel AF relay selection (RS) rules, namely, symbol error probability (SEP)-optimal, inverse-of-affine (IOA), and linear rules. We analyze the SEPs of the IOA and linear rules and also develop a novel, accurate approximation technique for analyzing the performance of AF relays. Extensive numerical results show that all the three rules outperform several RS rules proposed in the literature and generalize the conventional AF RS rule. [ABSTRACT FROM PUBLISHER]

Published
2015
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