Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

While the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference i.e. reduction in model size, latency and energy consumption. A technique to limit model size is quantization, i.e. using fewer bits to represent weights and biases. Such an approach usually results in a decline in performance. Here, we introduce a novel method for designing optimally heterogeneously quantized versions of deep neural network models for minimum-energy, high-accuracy, nanosecond inference and fully automated deployment on chip. With a per-layer, per-parameter type automatic quantization procedure, sampling from a wide range of quantizers, model energy consumption and size are minimized while high accuracy is maintained. This is crucial for the event selection procedure in proton-proton collisions at the CERN Large Hadron Collider, where resources are strictly limited and a latency of ${\mathcal O}(1)~\mu$s is required. Nanosecond inference and a resource consumption reduced by a factor of $50$ when implemented on FPGA hardware is achieved.