Search

We report on the design and the construction of a sounding rocket payload capable of performing atom interferometry with Bose-Einstein condensates of $^{41}$K and $^{87}$Rb. The apparatus is designed to be launched in two consecutive missions with a VSB-30 sounding rocket and is qualified to withstand the expected vibrational loads of 1.8 g root-mean-square in a frequency range between 20 - 2000 Hz and the expected static loads during ascent and re-entry of 25 g. We present a modular design of the scientific payload comprising a physics package, a laser system, an electronics system and a battery module. A dedicated on-board software provides a largely automated process of predefined experiments. To operate the payload safely in laboratory and flight mode, a thermal control system and ground support equipment has been implemented and will be presented. The payload presented here represents a cornerstone for future applications of matter wave interferometry with ultracold atoms on satellites.

Spiking Neural Networks (SNNs) compute in an event-based matter to achieve a more efficient computation than standard Neural Networks. In SNNs, neuronal outputs (i.e. activations) are not encoded with real-valued activations but with sequences of binary spikes. The motivation of using SNNs over conventional neural networks is rooted in the special computational aspects of SNNs, especially the very high degree of sparsity of neural output activations. Well established architectures for conventional Convolutional Neural Networks (CNNs) feature large spatial arrays of Processing Elements (PEs) that remain highly underutilized in the face of activation sparsity. We propose a novel architecture that is optimized for the processing of Convolutional SNNs (CSNNs) that feature a high degree of activation sparsity. In our architecture, the main strategy is to use less but highly utilized PEs. The PE array used to perform the convolution is only as large as the kernel size, allowing all PEs to be active as long as there are spikes to process. This constant flow of spikes is ensured by compressing the feature maps (i.e. the activations) into queues that can then be processed spike by spike. This compression is performed in run-time using dedicated circuitry, leading to a self-timed scheduling. This allows the processing time to scale directly with the number of spikes. A novel memory organization scheme called memory interlacing is used to efficiently store and retrieve the membrane potentials of the individual neurons using multiple small parallel on-chip RAMs. Each RAM is hardwired to its PE, reducing switching circuitry and allowing RAMs to be located in close proximity to the respective PE. We implemented the proposed architecture on an FPGA and achieved a significant speedup compared to other implementations while needing less hardware resources and maintaining a lower energy consumption., Comment: 12 pages, 12 figures, 5 tables, submitted to CODES 2022

The number of Digital Signal Processor (DSP) resources available in Field Programmable Gate Arrays (FPGAs) is often quite limited. Therefore, full utilization of available DSP resources for the computationally intensive parts of an algorithm is paramount for optimizing the non-functional properties of an implementation (i.e., performance, power, and area). The DSPs available in Xilinx devices implement large bit width operators (i.e. a 48-bit accumulator or a $18 \times 27$ multiplier). However, using such a DSP for low-precision quantized data (as is common in image processing or machine learning applications) leaves the DSP resources underutilized. As a remedy, A method has been proposed to pack and compute four 4-bit multiplications on a single DSP in a single clock cycle. This paper presents a generalization of this scheme to arbitrary bit widths and number of multiplications. We also demonstrate that the previously proposed approach leads to errors (Mean Absolute Error (MAE) = 0.37). Furthermore, we explain where these errors come from and how they can be corrected. On top, we introduce a novel approximate method called "Overpacking" which allows to squeeze even more multiplications into a single DSP at the cost of small errors (MAE = 0.47). Overpacking allows to squeeze six 4-bit multiplications into a single DSP compared to just four in the literature. Finally, we introduce an alternative method for packing multiple small-bit width additions into a single 48-bit accumulator for use in applications such as Spiking Neural Networks., Comment: 7 pages, 9 figures, 3 tables, submitted to FPL 2022

Microgravity eases several constraints limiting experiments with ultracold and condensed atoms on ground. It enables extended times of flight without suspension and eliminates the gravitational sag for trapped atoms. These advantages motivated numerous initiatives to adapt and operate experimental setups on microgravity platforms. We describe the design of the payload, motivations for design choices, and capabilities of the Bose-Einstein Condensate and Cold Atom Laboratory (BECCAL), a NASA-DLR collaboration. BECCAL builds on the heritage of previous devices operated in microgravity, features rubidium and potassium, multiple options for magnetic and optical trapping, different methods for coherent manipulation, and will offer new perspectives for experiments on quantum optics, atom optics, and atom interferometry in the unique microgravity environment on board the International Space Station.