Yasir Mahmood Qureshi

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The expeditious proliferation of Internet connectivity and the growing adoption of digital products have transformed various spheres of our everyday lives. This increased digitization of society has led to the emergence of new applications, which are deplo ...

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications’ run-time. ...

Next generation workloads, such as genome sequencing, have an astounding impact in the healthcare sector. Sequence alignment, the first step in genome sequencing, has experienced recent breakthroughs, which resulted in next generation sequencing (NGS). As ...

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

The increasing ubiquity of edge devices in the consumer market, along with their ever more computationally expensive workloads, necessitate corresponding increases in computing power to support such workloads. In-memory computing is attractive in edge devi ...

The need for efficient Convolutional Neural Network (CNNs) targeting embedded systems led to the popularization of Binary Neural Networks (BNNs), which significantly reduce execution time and memory requirements by representing the operands using only one ...

The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this p ...

The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this paper, ...

Cloud Computing aims to efficiently tackle the increasing demand of computing resources, and its popularity has led to a dramatic increase in the number of computing servers and data centers worldwide. However, as effect of post-Dennard scaling, computing ...

In order to develop sustainable and more powerful information technology (IT) infrastructures, the challenges posed by the "memory wall" are critical for the design of high-performance and high-efficiency many-core computing systems. In this context, recen ...

Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spati ...

Yasir Mahmood Qureshi

Chattez avec Graph Search

Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

BLADE: A BitLine Accelerator for Devices on the Edge

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

Gem5-X : A Many-Core Heterogeneous Simulation Platform for Architectural Exploration and Optimization

Energy Proportionality in Near-Threshold Computing Servers and Cloud Data Centers: Consolidating or Not?

Full System Exploration of On-Chip Wireless Communication on Many-Core Architectures

Blade: An in-Cache Computing Architecture for Edge Devices