1. Machine Learning at the Edge
High velocity data inferencing
Audrey Corbeil Therrien
Omar Quijano
Averell Gatton
Ryan Coffee

2. High velocity data - Timetool
us latency
Schematic of timetool to laser sync
us latency
Image of timetool waveform?

3. High velocity data - CookieBox
Schematic cookiebox
Downstream veto + time binning
Image of cookie box single pulse, double pulse

4. Workflow Diagram of relationship between FPGA-GPU-CPU and their roles
FPGA fast inference - ultimately ASIC?
GPU for training the model
CPU for the physics simulations providing ground truth - understanding physics of corner case
Send corner case back for training
Smart initializing - Model library
Confidence metric - selects corner cases for further trainig, recommends reloading a new model to scientists when off track

5. Workflow Diagram of relationship between FPGA-GPU-CPU and their roles
FPGA fast inference - ultimately ASIC?
GPU for training the model
CPU for the physics simulations providing ground truth - understanding physics of corner case
Send corner case back for training
Smart initializing - Model library
Confidence metric - selects corner cases for further trainig, recommends reloading a new model to scientists when off track

6. Hardware CPU Simulation GPU Simulation FPGA 2TB NVMe NVIDIA Tesla P4
Dual CPU:
Intel Xeon Gold 6148
20 Physical
40 Logical
2.4 GH at TDP 150W
3.7 GHz TB
RAM:
764 GB DDR4
GPU Simulation
NVIDIA Tesla P4
(Inferencing Accelerator)
5.5 TF
15x IL
60X EF
NVIDIA Tesla P40
(GPU Accelerator)
3,840 cores
24 GB GDDR5
NVIDIA Tesla V100
640 Tensor Cors
5,120 Cores
32 GB
FPGA
KCU 1500
Hardware Accelerator
2TB NVMe

7. Software

8. Timeline Present CPU Simulation In Progress GPU Simulation CUDA 10
cuDNN 7.4
NCCL 2.4
TensorRT 5.0
In Progress
FPGA
JDK 1.8
Scala 2.12
Spatial
sbt
Milestone
Pod Integration
Fast Communication
NVMe Cache
Visualization
Left: spatially multiplexed 2D timetool signal (January 2018)
Right: Result of the convolution with the simulated waveform.
50% RAM / 80 CPU
Data visualzation for interactivr mode

9. Conclusion Detectors provide data, users need information
ML can convert data into information with low latency and act on the information to
veto bad shots
bin specific cases
protect detectors
We are building the software and hardware architecture for this objective
Technology extendable to other detectors
Poster presentation - Using FPGA for fast inferencing