7 papers
FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design
Jiahao Zhang, Zifan He, Nicholas Fraser +3
We present FlexLLM, a composable High-Level Synthesis (HLS) library for rapid development of domain-specific LLM accelerators. FlexLLM exposes key architectural degrees of freedom…
SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Accelerators
Yaman Umuroglu, Christoph Berganski, Felix Jentzsch +8
While neural network quantization effectively reduces the cost of matrix multiplications, aggressive quantization can expose non-matrix-multiply operations as significant performan…
FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs
Shashwat Khandelwal, Jakoba Petri-Koenig, Thomas B. Preußer +2
Recurrent neural networks (RNNs), particularly LSTMs, are effective for time-series tasks like sentiment analysis and short-term stock prediction. However, their computational comp…
Improving Quantization with Post-Training Model Expansion
Giuseppe Franco, Pablo Monteagudo-Lago, Ian Colbert +2
The size of a model has been a strong predictor of its quality, as well as its cost. As such, the trade-off between model cost and quality has been well-studied. Post-training opti…
Optimizing Communication for Latency Sensitive HPC Applications on up to 48 FPGAs Using ACCL
Marius Meyer, Tobias Kenter, Lucian Petrica +3
Most FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization o…
ACCL+: an FPGA-Based Collective Engine for Distributed Applications
Zhenhao He, Dario Korolija, Yu Zhu +5
FPGAs are increasingly prevalent in cloud deployments, serving as Smart NICs or network-attached accelerators. Despite their potential, developing distributed FPGA-accelerated appl…
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
Shivam Aggarwal, Hans Jakob Damsgaard, Alessandro Pappalardo +4
Post-training quantization (PTQ) is a powerful technique for model compression, reducing the numerical precision in neural networks without additional training overhead. Recent wor…