On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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6 results for “hls4ml”

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4mlcs.LG

Paper presents FPGA implementation for efficient recurrent neural networks.

problem Implementing recurrent neural networks on FPGAs for low latency.

method Developed hls4ml framework to implement LSTM and GRU layers.

result Demonstrated effective designs for both small and large models.

FPGAs Recurrent Neural Networks Low Latency hls4ml Physics Applications

2022-07-01abs ↗pdf ↗

Fast convolutional neural networks on FPGAs with hls4mlcs.LG

Automated tool reduces FPGA inference latency to 5 μs for deep neural networks.

problem Deploying ultra low-latency, low-power deep neural networks on FPGAs.

method Extending hls4ml library, using model compression techniques like pruning and quantization-aware training.

result Achieved inference latency of 5 μs with 97% resource reduction.

FPGAs Convolutional Neural Networks Model Compression Low Latency Deep Learning

2021-01-13abs ↗pdf ↗

QONNX: Representing Arbitrary-Precision Quantized Neural Networkscs.LG

Extends ONNX for quantized neural networks with new formats and operators.

problem Handling arbitrary-precision quantization in neural networks.

method Introduces new formats and operators in ONNX to represent quantized neural networks.

result Enabled representation of uniform quantization in neural networks.

quantized neural networks ONNX arbitrary precision quantization clipping

2022-06-15abs ↗pdf ↗

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcs.CV

Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.

problem Efficient real-time semantic segmentation for autonomous vehicles.

method Compressed ENet architecture, FPGA deployment, batch processing, filter reduction, quantization-aware training.

result Reduced latency to 3 ms per image with batch size of ten and 40% resource utilization.

FPGAs autonomous vehicles semantic segmentation ENet Cityscapes

2022-05-16abs ↗pdf ↗

Graph Neural Networks for Charged Particle Tracking on FPGAsphysics.ins-det

Graph neural networks improve charged particle tracking on FPGAs.

problem Charged particle trajectory determination in high interaction density conditions.

method Graph neural networks (GNNs) embedded in tracker data as graphs, classifying edges as track segments.

result GNNs implemented on FPGAs for charged particle tracking, enabling future HL-LHC experiments.

Graph Neural Networks Charged Particle Tracking Field-Programmable Gate Arrays High-Luminosity LHC Trigger Level

2021-12-03abs ↗pdf ↗

Fast inference of deep neural networks in FPGAs for particle physicsphysics.ins-det

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the u…

2018-04-16abs ↗pdf ↗