A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC. (arXiv:2105.01683v1 [physics.ins-det])

Despite advances in the programmable logic capabilities of modern trigger
systems, a significant bottleneck remains in the amount of data to be
transported from the detector to off-detector logic where trigger decisions are
made. We demonstrate that a neural network autoencoder model can be implemented
in a radiation tolerant ASIC to perform lossy data compression alleviating the
data transmission problem while preserving critical information of the detector
energy profile. For our application, we consider the high-granularity
calorimeter from the CMS experiment at the CERN Large Hadron Collider. The
advantage of the machine learning approach is in the flexibility and
configurability of the algorithm. By changing the neural network weights, a
unique data compression algorithm can be deployed for each sensor in different
detector regions, and changing detector or collider conditions. To meet area,
performance, and power constraints, we perform a quantization-aware training to
create an optimized neural network hardware implementation. The design is
achieved through the use of high-level synthesis tools and the hls4ml
framework, and was processed through synthesis and physical layout flows based
on a LP CMOS 65 nm technology node. The flow anticipates 200 Mrad of ionizing
radiation to select gates, and reports a total area of 3.6 mm^2 and consumes 95
mW of power. The simulated energy consumption per inference is 2.4 nJ. This is
the first radiation tolerant on-detector ASIC implementation of a neural
network that has been designed for particle physics applications.