Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the “power-term approximator block”, consisting of logarithmic and exponential activation functions. GINN-LP is end-to-end differentiable, making it possible to use backpropagation for training. We propose a neural network growth strategy that will enable finding the suitable number of terms in the Laurent polynomial that represents the data, along with sparsity regularization to promote the discovery of concise equations. To the best of our knowledge, this is the first model that can discover arbitrary multivariate Laurent polynomial terms without any prior information on the order. Our approach is first evaluated on a subset of data used in SRBench, a benchmark for symbolic regression. We first show that GINN-LP outperforms the state-of-the-art symbolic regression methods on datasets generated using 48 real-world equations in the form of multivariate Laurent polynomials. Next, we propose an ensemble method that combines our method with a high-performing symbolic regression method, enabling us to discover non-Laurent polynomial equations. We achieve state-of-the-art results in equation discovery, showing an absolute improvement of 7.1% over the best contender, by applying this ensemble method to 113 datasets within SRBench with known ground-truth equations.

SMTPE ST 2110 is a professional video over IP standard which enables uncompressed video to be transported as separate video, audio and ancillary data streams, allowing true flexibility in media workflows. Uncompressed video transmission allows professional video environments to achieve high quality video distribution while meeting strict latency requirements. Although this standard can be implemented on software, hardware solutions can achieve superior performance, lower latency and lower power consumption while efficiently handling jitter and packet bursts. This paper presents an FPGA based hardware implementation of a SMPTE ST 2110 compliant end to end solution supporting uncompressed video resolutions of up to 4K at 30fps. Due to its scalability, the architecture can be easily extended to support higher video resolutions and/or multiple video streams. Additionally, the interface modularity of the architecture enables integration with any video interface type. In this work, a Serial Digital Interface (SDI) interface is used. The proposed architecture consists of a replicable video processing pipeline for each stream, a Session Description Protocol (SDP) compliant network configuration unit and a SMPTE ST 2059-1/2 compliant time synchronization unit common to all streams, with network access via a 10G UDP/IPv4/Ethernet stack. All modules other than the Ethernet core and SDI interface modules can operate at 200MHz even though 4K 30fps video processing can be achieved with a minimum operating frequency of 100MHz.