Search Results for author:
Found 6 papers, 2 papers with code
Allo: A Programming Model for Composable Accelerator Design
For the GPT2 model, the inference latency of the Allo generated accelerator is 1. 7x faster than the NVIDIA A100 GPU with 5. 4x higher energy efficiency, demonstrating the capability of Allo to handle large-scale designs.
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.
Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training
Specifically, Slapo works on a PyTorch model and uses a set of schedule primitives to convert the model for common model training optimizations such as high-performance kernels, effective 3D parallelism, and efficient activation checkpointing.
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.
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
We design an efficient FPGA-based accelerator for our novel BNN model that supports the fractional activations.
