Titlebook: Design and Architectures for Signal and Image Processing; 17th International W Tiago Dias,Paola Busia Conference proceedings 2024 The Edito

Haphazard

LOPE

acrimony

Improving the?Energy Efficiency of?CNN Inference on?FPGA Using Partial Reconfigurationnced solutions for the deployment of highly energy-efficient implementations. This paper presents a novel approach to improve the efficiency of CNN inference on Field-Programmable Gate Arrays (FPGAs) using Partial Reconfiguration (PR). Our method deconstructs CNN topology into different layers for r

Offstage

Optimising Graph Representation for?Hardware Implementation of?Graph Convolutional Networks for?Evenst proposals in this area are Graph Convolutional Networks (GCNs), which allow to process events in its original sparse form while maintaining high detection and classification performance. In this paper, we present the hardware implementation of a?graph generation process from an event camera data

積習難改

平項山

Scratchy: A Class of?Adaptable Architectures with?Software-Managed Communication for?Edge Streaming s different topology options and is demonstrated using a 3-core Scratchy. The capabilities of the architecture are presented through a design space exploration that focuses on optimizing the topology for specific applications. It also highlights the low resource overhead of the architecture and quic

Gourmet

Pelago

Improving the?Energy Efficiency of?CNN Inference on?FPGA Using Partial Reconfigurationic hardware implementations, respectively. These results also show that the benefits of PR improve with the depth of the network, suggesting very promising levels of gains as the network gets larger and under the key conditions of using fast optimized reconfiguration controllers and methodical syste

Influx

思想