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Found 5 papers, 0 papers with code
Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference
Given the high compute and memory requirements of modern LLMs, more and more top-of-the-line GPUs are being deployed to serve these models.
SENSEi: Input-Sensitive Compilation for Accelerating GNNs
We leverage this observation to propose SENSEi, a system that exposes different sparse and dense matrix primitive compositions based on different matrix re-associations of GNN computations and selects the best among them based on input attributes.
Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation
To address this problem, this paper explores using Adversarial Machine Learning (AML) methods as a defense at the computer architecture layer to obfuscate side channels.
SparseTrain:Leveraging Dynamic Sparsity in Training DNNs on General-Purpose SIMD Processors
Our community has greatly improved the efficiency of deep learning applications, including by exploiting sparsity in inputs.
Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures
A DNN's architecture (i. e., its hyper-parameters) broadly determines the DNN's accuracy and performance, and is often confidential.
