Summary of Multiple Benchmarks on the High Performance Data Processor (HPDP)

The purpose of this communication is to discuss on the suitability of the HPDP device for power-efficient, on-board data processing in future satellites. Five use cases will be presented. These use cases have been analysed in the course of a GSTP de-risk activity or in the early phases of scientific missions. The performance figures reported below have been obtained on the HPDP evaluation board connected with a laptop.
Moon Asteroid Strike (MAS): The MAS algorithm is meant to detect and count the asteroids striking the moon surface. Basically, the idea is to count collision flashes in the moon surface and from their intensity and duration to extract meaningful information about the collision energy and the mass of the asteroid. From the computational point of view the algorithm involves DMA engines, filtering, storage of the last 7 frames and comparison. The computational flow is shown hereinafter. The obtained performance is the on-the-fly analysis of 116 HD fps at the expense of 1.65W power consumption. This implementation is meant to be used in the Lumio mission.
Vessel Detection (VD) from EO images: The VD images is meant to detect vessels from EO images. At first, the VD algorithm involves a Sobel filter. This is actually an edge detection filter, which amplifies the various features of the image. The Sobel operator consists of a pair of 3x3 convolution kernels and is designed to perform 2D spatial gradient over an image. In turn, 6 kernels of 20x20 pixels each are applied to the image and after comparison with thresholds the detected vessels are reported. This implementation can process 9.6 HD fps at the expenses of 1.65W power consumption.
A step further, a machine learning version of this application has been developed in tensor flow. Targeting >90% correct answers for the application of the VD, the NN is trained outside the HPDP device by using the tensor flow framework and python and the final NN is mapped on the HPDP in order to perform the pattern recognition. For this application a convolutional network is chosen with 1 hidden layer and one final dense layer of neurons. In total, the network has about 5 thousand parameters and it is mapped in 2-3 different configurations of the HPDP array. The different configurations are applied on the fly with a delay of less than 0.5ms per reconfiguration. The obtained results are better than those presented above in terms of accuracy, the processed fps figure is similar as above and the power consumption remains idem.
AES 256: Two versions of the encryption algorithm have been developed: AES256 without CBC (Cipher Block Chaining) and AES256 with CBC. First the key expansion is calculated in the FNC0 and is passed via a FIFO to the array. The key expansion process generates the expanded version of the 256b key which is used for the actual encryption. The expanded key is used repeatedly throughout the encryption process and it is stored in the FIFO of the array in order to reduce additional delays. The data to be encrypted are fetched to the incoming stream by the DMA in groups of 4 bytes. From the other side, the cypher sends data to the on-chip SRAM in packets of 4 bytes too. Two instances of the AES256 without CBC IP (resp. One instance of the AES256 IP with CDC) can fit in the array giving a total throughput of 11.7MB/s (resp. 5MB/s) at the expense of 1.65W power consumption.
Image Compression: For the compression of the multi-spectral imagery the lossless flavor of the standard CCSDS 123.0-B-2 has been implemented. The compression is split into two sequential configurations and the attained throughput is 1Gb/s image data consumption. In this configuration the power consumption of the device is 1.65W and no external processing capability is required. Currently ISD is working on similar lossless algorithms featuring low entropy encoder targeting the TRUTHS mission.