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Found 11 papers, 5 papers with code
CRISP: Hybrid Structured Sparsity for Class-aware Model Pruning
Machine learning pipelines for classification tasks often train a universal model to achieve accuracy across a broad range of classes.
Post-Training Quantization with Low-precision Minifloats and Integers on FPGAs
However, the exploration of floating-point formats smaller than 8 bits and their comparison with integer quantization remains relatively limited.
InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update
Our experiments with three GNN models on four large graphs demonstrate that InkStream accelerates by 2. 5-427$\times$ on a CPU cluster and 2. 4-343$\times$ on two different GPU clusters while producing identical outputs as GNN model inference on the latest graph snapshot.
Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs
Coarse-Grained Reconfigurable Arrays (CGRAs) hold great promise as power-efficient edge accelerator, offering versatility beyond AI applications.
Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
In particular, we design a Variational Autoencoder (VAE) with a training objective that is customized to learn the synthetic data representations optimally.
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data
Moreover, the sample generation strategies in some of these methods could result in a mismatch between the synthetic and real data distributions.
KLEESPECTRE: Detecting Information Leakage through Speculative Cache Attacks via Symbolic Execution
Spectre attacks disclosed in early 2018 expose data leakage scenarios via cache side channels.
Cryptography and Security
Neural Network Inference on Mobile SoCs
Mobile devices are empowered with heterogeneous multi-processor Systems-on-Chips (SoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference.
High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself.
oo7: Low-overhead Defense against Spectre Attacks via Program Analysis
In this paper, we propose oo7, a static analysis approach that can mitigate Spectre attacks by detecting potentially vulnerable code snippets in program binaries and protecting them against the attack by patching them.
Cryptography and Security
Synergy: A HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC
Convolutional Neural Networks (CNN) have been widely deployed in diverse application domains.
