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Found 5 papers, 0 papers with code
Zero-Space Cost Fault Tolerance for Transformer-based Language Models on ReRAM
Resistive Random Access Memory (ReRAM) has emerged as a promising platform for deep neural networks (DNNs) due to its support for parallel in-situ matrix-vector multiplication.
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation
To address this shortcoming, we propose a novel KD approach to GNN compression that we call Attention-Based Knowledge Distillation (ABKD).
Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity
Traditional computer vision models often necessitate extensive data acquisition, annotation, and validation.
Subgraph Stationary Hardware-Software Inference Co-Design
For the stream of queries, SUSHI yields up to 25% improvement in latency, 0. 98% increase in served accuracy.
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
With weights stored in the ReRAM crossbar cells as conductance, when the input vector is applied to word lines, the matrix-vector multiplication results can be generated as the current in bit lines.
