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Found 9 papers, 6 papers with code
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs.
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
Experimental results demonstrate our approach can achieve up to 13. 4x speedup when compared to previous FPGA-based accelerators for the BERT model.
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
This work studies post-training parameter quantization in large language models (LLMs).
Structured Pruning is All You Need for Pruning CNNs at Initialization
In addition, since PreCropping compresses CNNs at initialization, the computational and memory costs of CNNs are reduced for both training and inference on commodity hardware.
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model.
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Deploying deep learning models on embedded systems has been challenging due to limited computing resources.
Algorithm-hardware Co-design for Deformable Convolution
In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications including full versus depthwise, fixed-shape, and limited-range.
ZeroQ: A Novel Zero Shot Quantization Framework
Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0. 5\% of one epoch training time of ResNet50 on ImageNet).
Ranked #1 on
Data Free Quantization
on CIFAR10
(CIFAR-10 W8A8 Top-1 Accuracy metric)
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
However, the search space for a mixed-precision quantization is exponential in the number of layers.
