9 citations · 20 across the 6 of their papers we have counts for
7 papers
Error Diffusion: Post Training Quantization with Block-Scaled Number Formats for Neural Networks
Alireza Khodamoradi, Kristof Denolf, Eric Dellinger
Quantization reduces the model's hardware costs, such as data movement, storage, and operations like multiply and addition. It also affects the model's behavior by degrading the ou…
Microscaling Data Formats for Deep Learning
Bita Darvish Rouhani, Ritchie Zhao, Ankit More +30
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that…
SPARTA: Spatial Acceleration for Efficient and Scalable Horizontal Diffusion Weather Stencil Computation
Gagandeep Singh, Alireza Khodamoradi, Kristof Denolf +6
Fast and accurate climate simulations and weather predictions are critical for understanding and preparing for the impact of climate change. Real-world weather and climate modeling…
CHARM: Composing Heterogeneous Accelerators for Matrix Multiply on Versal ACAP Architecture
Jinming Zhuang, Jason Lau, Hanchen Ye +10
Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogene…
Tailor: Altering Skip Connections for Resource-Efficient Inference
Olivia Weng, Gabriel Marcano, Vladimir Loncar +7
Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and of…
RUBICON: A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Gagandeep Singh, Mohammed Alser, Kristof Denolf +5
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The a…
Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels
Murad Qasaimeh, Kristof Denolf, Jack Lo +3
Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embe…