Presentation
Athena: Add More Intelligence to RMT-based Network Data Plane with Low-bit Quantization
DescriptionPerforming per-packet Neural Network (NN) inference on the network data plane is required for high-quality and fast decision-making in computer networking. However, data plane architecture like the Reconfigurable Match Tables (RMT) pipeline has limited support for NN. Previous efforts have utilized Binary Neuron Networks (BNNs) as a compromise, but the accuracy loss of BNN is high. Inspired by the accuracy gain of the two-bit model. this paper proposes Athena. Athena can deploy the sparse low-bit quantization (two-bit and four-bit) model on RMT. Compared with the BNN-based state-of-the-art, Athena is cost-effective regarding accuracy loss reduction, inference latency, and chip area overhead.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security