74 citations · 200 across the 17 of their papers we have counts for
21 papers
SPARTA: Spatial Acceleration for Efficient and Scalable Horizontal Diffusion Weather Stencil Computation
Gagandeep Singh, Alireza Khodamoradi, Kristof Denolf +6
Fast and accurate climate simulations and weather predictions are critical for understanding and preparing for the impact of climate change. Real-world weather and climate modeling…
BOMP-NAS: Bayesian Optimization Mixed Precision NAS
David van Son, Floran de Putter, Sebastian Vogel +1
Bayesian Optimization Mixed-Precision Neural Architecture Search (BOMP-NAS) is an approach to quantization-aware neural architecture search (QA-NAS) that leverages both Bayesian op…
THOR -- A Neuromorphic Processor with 7.29G TSOP/mmJs Energy-Throughput Efficiency
Mayank Senapati, Manil Dev Gomony, Sherif Eissa +2
Neuromorphic computing using biologically inspired Spiking Neural Networks (SNNs) is a promising solution to meet Energy-Throughput (ET) efficiency needed for edge computing device…
BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC
Maarten Molendijk, Floran de Putter, Manil Gomony +2
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut do…
Low- and Mixed-Precision Inference Accelerators
Maarten Molendijk, Floran de Putter, Henk Corporaal
With the surging popularity of edge computing, the need to efficiently perform neural network inference on battery-constrained IoT devices has greatly increased. While algorithmic…
How to train accurate BNNs for embedded systems?
Floran de Putter, Henk Corporaal
A key enabler of deploying convolutional neural networks on resource-constrained embedded systems is the binary neural network (BNN). BNNs save on memory and simplify computation b…
Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors
Wei Sun, Ang Li, Tong Geng +2
Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users ha…
Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
Gagandeep Singh, Rakesh Nadig, Jisung Park +7
Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques t…
FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications
Gagandeep Singh, Mohammed Alser, Damla Senol Cali +4
Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both exec…
Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric
Gagandeep Singh, Dionysios Diamantopoulos, Juan Gómez-Luna +4
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 impl…
NMPO: Near-Memory Computing Profiling and Offloading
Stefano Corda, Madhurya Kumaraswamy, Ahsan Javed Awan +3
Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a mode…
How Flexible is Your Computing System
Shihua Huang, Luc Waeijen, Henk Corporaal
In literature computer architectures are frequently claimed to be highly flexible, typically implying there exist trade-offs between flexibility and performance or energy efficienc…
NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling
Gagandeep Singh, Dionysios Diamantopoulos, Christoph Hagleitner +4
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 impl…
TDO-CIM: Transparent Detection and Offloading for Computation In-memory
Kanishkan Vadivel, Lorenzo Chelini, Ali BanaGozar +4
Computation in-memory is a promising non-von Neumann approach aiming at completely diminishing the data transfer to and from the memory subsystem. Although a lot of architectures h…
Near Memory Acceleration on High Resolution Radio Astronomy Imaging
Stefano Corda, Bram Veenboer, Ahsan Javed Awan +3
Modern radio telescopes like the Square Kilometer Array (SKA) will need to process in real-time exabytes of radio-astronomical signals to construct a high-resolution map of the sky…
Quantization of Deep Neural Networks for Accumulator-constrained Processors
Barry de Bruin, Zoran Zivkovic, Henk Corporaal
We introduce an Artificial Neural Network (ANN) quantization methodology for platforms without wide accumulation registers. This enables fixed-point model deployment on embedded co…
OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation
Michel van Lier, Alexios Balatsoukas-Stimming, Henk Corporaaal +1
The use of machine learning methods to tackle challenging physical layer signal processing tasks has attracted significant attention. In this work, we focus on the use of neural ne…
Near-Memory Computing: Past, Present, and Future
Gagandeep Singh, Lorenzo Chelini, Stefano Corda +5
The conventional approach of moving data to the CPU for computation has become a significant performance bottleneck for emerging scale-out data-intensive applications due to their…
Platform Independent Software Analysis for Near Memory Computing
Stefano Corda, Gagandeep Singh, Ahsan Javed Awan +2
Near-memory Computing (NMC) promises improved performance for the applications that can exploit the features of emerging memory technologies such as 3D-stacked memory. However, it…
Memory and Parallelism Analysis Using a Platform-Independent Approach
Stefano Corda, Gagandeep Singh, Ahsan Javed Awan +2
Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, de…
LocalNorm: Robust Image Classification through Dynamically Regularized Normalization
Bojian Yin, Siebren Schaafsma, Henk Corporaal +2
While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, compared to humans, much more sensitive to image degradation.…