Related papers: PsPIN: A high-performance low-power architecture f…
Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computations while avoiding expensive multiplication operations. In…
Physics-informed neural networks (PINNs) have emerged as a powerful approach for solving partial differential equations (PDEs) by training neural networks with loss functions that incorporate physical constraints. In this work, we introduce…
With the widespread use of deep neural networks(DNNs) in intelligent systems, DNN accelerators with high performance and energy efficiency are greatly demanded. As one of the feasible processing-in-memory(PIM) architectures,…
The machine learning community has become increasingly interested in the energy efficiency of neural networks. The Spiking Neural Network (SNN) is a promising approach to energy-efficient computing, since its activation levels are quantized…
This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increased rapidly, pushing the size and thus the memory…
Ensuring energy-efficient design in neuromorphic computing systems necessitates a tailored architecture combined with algorithmic approaches. This manuscript focuses on enhancing brain-inspired perceptual computing machines through a novel…
The performance of discrete general purpose graphics processing units (GPGPUs) has been improving at a rapid pace. The PCIe interconnect that controls the communication of data between the system host memory and the GPU has not improved as…
Today's datacenter applications rely on datastores that are required to provide high availability, consistency, and performance. To achieve high availability, these datastores replicate data across several nodes. Such replication is managed…
We propose EdgeSpike, a co-designed spiking neural network (SNN) framework for autonomous low-power sensing in edge Internet of Things (IoT) architectures. EdgeSpike unifies (i) a hybrid surrogate-gradient and direct-encoding training…
The emergence of ultra-low latency applications, such as financial transactions, has driven the development of hybrid backbone networks that rely on fiber, satellite, and microwave links. Despite providing low latencies, these hybrid…
This research work proposes a design of an analog ReRAM-based PIM (processing-in-memory) architecture for fast and efficient CNN (convolutional neural network) inference. For the overall architecture, we use the basic hardware hierarchy…
We present a portable platform, called PIC_ENGINE, for accelerating Particle-In-Cell (PIC) codes on heterogeneous many-core architectures such as Graphic Processing Units (GPUs). The aim of this development is efficient simulations on…
Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities, from a few kOPS to GOPS. Near-threshold(NT) operation can…
The nanoPU is a new networking-optimized CPU designed to minimize tail latency for RPCs. By bypassing the cache and memory hierarchy, the nanoPU directly places arriving messages into the CPU register file. The wire-to-wire latency through…
Applications such as holographic concerts are now emerging. However, their provisioning remains highly challenging. Requirements such as high bandwidth and ultra-low latency are still very challenging for the current network infrastructure.…
Spiking Neural Networks (SNNs), with brain-inspired structure using discrete spikes instead of continuous activations, are gaining attention for their efficient processing on neuromorphic chips. While current SNN hardware accelerators often…
Software Defined Networking (SDN) is a new networking architecture which aims to provide better decoupling between network control (control plane) and data forwarding functionalities (data plane). This separation introduces several…
Near-data in-storage processing research has been gaining momentum in recent years. Typical processing-in-storage architecture places a single or several processing cores inside the storage and allows data processing without transferring it…
Spiking Neural Networks (SNNs) are one of the most promising bio-inspired neural networks models and have drawn increasing attention in recent years. The event-driven communication mechanism of SNNs allows for sparse and theoretically…
This paper presents a high-performance, scalable network monitoring and intrusion detection system (IDS) implemented in P4. The proposed solution is designed for high-performance environments such as cloud data centers, where ultra-low…