Offloading compute-intensive kernels to hardware accelerators relies on the large degree of parallelism offered by these platforms. However, the effective bandwidth of the memory interface often causes a bottleneck, hindering the accelerator’s effective performance. Techniques enabling data reuse, such as tiling, lower the pressure on memory traffic but do not fully exploit the bandwidth. A further increase in bandwidth utilization is possible by using burst rather than element-wise accesses, provided the data is contiguous in memory. In this article, we propose a memory allocation technique, and provide a proof-of-concept source-to-source compiler pass, that enables such burst transfers by modifying the data layout in external memory. Our experiments show the new memory allocation yields close to 100% bandwidth utilization while the memory engines occupy less than 5% of the field-programmable gate array (FPGA) logic area.

In our demo, we present two hardware platforms for prototyping audio array signal processing. Pyramic is a 48-channel microphone array fitted on an FPGA and Compact Six is a portable microphone array with six microphones, closer to the technical constraints of consumer electronics. A browser based interface was developed that allows the user to interact with the audio stream from the arrays in real time. The software component of this demo is a Python module with implementations of basic audio signal processing blocks and popular techniques like STFT, beamforming, and DoA. Both the hardware design files and the software are open source and freely shared. As part of a collaboration with IBM Research, their beamforming and imaging technologies will also be portrayed. The hardware will be demonstrated through an installation processing the microphone signals into light patterns on a circular LED array. The demo will be interactive and let visitors play with different algorithms for DoA (SRP, FRIDA [1], Bluebild) and beamforming (MVDR, Flexibeam [2]). The availability of an open platform with reference implementations encourages reproducible research and minimizes setup-time when testing and benchmarking new audio array signal processing algorithms. It can also serve as a useful educational tool, providing a means to work with real-life signals.