  High-energy large-scale particle colliders produce data at high speed in the
order of 1 terabytes per second in nuclear physics and petabytes per second in
high-energy physics. Developing real-time data compression algorithms to reduce
such data at high throughput to fit permanent storage has drawn increasing
attention. Specifically, at the newly constructed sPHENIX experiment at the
Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as
the main tracking detector, which records particle trajectories in a volume of
a three-dimensional (3D) cylinder. The resulting data are usually very sparse
with occupancy around 10.8%. Such sparsity presents a challenge to conventional
learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D
convolutional neural network (CNN)-based approach, Bicephalous Convolutional
Autoencoder (BCAE), outperforms traditional methods both in compression rate
and reconstruction accuracy. BCAE can also utilize the computation power of
graphical processing units suitable for deployment in a modern heterogeneous
high-performance computing environment. This work introduces two BCAE variants:
BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77%
better reconstruction accuracy measured in mean absolute error compared with
BCAE. BCAE-2D treats the radial direction as the channel dimension of an image,
resulting in a 3x speedup in compression throughput. In addition, we
demonstrate an unbalanced autoencoder with a larger decoder can improve
reconstruction accuracy without significantly sacrificing throughput. Lastly,
we observe both the BCAE++ and BCAE-2D can benefit more from using
half-precision mode in throughput (76-79% increase) without loss in
reconstruction accuracy. The source code and links to data and pretrained
models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2.
