tag: DNN

Edge-AI Hardware for Extended Reality
By Technical Paper Link - 20 Jul, 2022 - Comments: 0

New technical paper titled "Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications" from researchers at Indian Institute of Technology Delhi and Reality Labs Research, Meta. Abstract "Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR w... » read more

HW/SW Co-Design to Configure DNN Models On Energy Harvesting Devices
By Technical Paper Link - 20 Jul, 2022 - Comments: 0

New technical paper titled "EVE: Environmental Adaptive Neural Network Models for Low-Power Energy Harvesting System" was published by researchers at UT San Antonio, University of Connecticut, and Lehigh University. According to the abstract: "This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for... » read more

Novel H2H mapping algorithm with both computation and communication awareness
By Technical Paper Link - 06 May, 2022 - Comments: 0

New research paper "H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness" from University of Pittsburgh, Georgia Tech. Abstract: "The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor perceiving and multi-task lear... » read more

Wavelength Multiplexed Ultralow-Power Photonic Edge Computing
By Technical Paper Link - 25 Mar, 2022 - Comments: 0

Abstract "Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-power devices, such as smartphones and sensors -- and this trend is accelerated by the simultaneous move towards Internet-of-Things (IoT) devices. Numerous efforts are underway to lower power consumption, but ... » read more

Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs
By Technical Paper Link - 14 Mar, 2022 - Comments: 0

Abstract: "When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make... » read more

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices
By Technical Paper Link - 20 Dec, 2021 - Comments: 0

Abstract:  "Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate i... » read more

Enabling Training of Neural Networks on Noisy Hardware
By Technical Paper Link - 18 Nov, 2021 - Comments: 0

Abstract:  "Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog hardware composed of resistive device arrays with non-symmetric conductance modulation characteristics. Recently we proposed a new algorithm, the Tiki-Taka algorithm, that overcomes t... » read more

FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
By Technical Paper Link - 19 Aug, 2021 - Comments: 0

Abstract: "Recent work demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication—the intensive and key computation in deep neural networks (DNNs). One key problem is the weights that are signed values. However, in a ReRAM crossbar, weights are stored as conductance of... » read more

REDUCT: Keep It Close, Keep It Cool – Scaling DNN Inference on Multi-Core CPUs with Near-Cache Compute
By Technical Paper Link - 02 Jul, 2021 - Comments: 0

Abstract—"Deep Neural Networks (DNN) are used in a variety of applications and services. With the evolving nature of DNNs, the race to build optimal hardware (both in datacenter and edge) continues. General purpose multi-core CPUs offer unique attractive advantages for DNN inference at both datacenter [60] and edge [71]. Most of the CPU pipeline design complexity is targeted towards optimizin... » read more

Xilinx AI Engines And Their Applications
By Xilinx - 31 Mar, 2021 - Comments: 0

This white paper explores the architecture, applications, and benefits of using Xilinx's new AI Engine for compute intensive applications like 5G cellular and machine learning DNN/CNN. 5G requires between five to 10 times higher compute density when compared with prior generations; AI Engines have been optimized for DSP, meeting both the throughput and compute requirements to deliver the hig... » read more

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