FPGA accelerated deep learning radio modulation classification using MATLAB system objects & PYNQ

Floating point Convolutional Neural Networks (CNNs) are computationally expensive and deeper networks can be impractical to deploy on FPGAs – consuming a large number of resources and power, as well as having lengthy development times. Previous work has hown that CNNs can be quantised heavily using fixed point arithmetic to combat this without significant loss in classification accuracy. We aim to quantise an existing CNN architecture or radio modulation classification to 2-bit weights and activations, while retaining a level of accuracy close to the original paper, for deployment on a Zynq System on Chip (SoC). To improve the development time for hardware synthesisable CNNs, we make use of MATLAB System Objects and HDL Coder. The PYNQ framework is presented as a practical means for accessing the functionality of the CNN. Our preliminary results show a high classification accuracy even with 2-bit weights and activations

Rapid Digital Architecture Design of Computationally Complex Algorithms

Traditional digital design techniques hardly keep up with the rising abundance of programmable circuitry found on recent Field-Programmable Gate Arrays. Therefore, the novel Rapid Data Type-Agnostic Digital Design Methodology (RDAM) elevates the design perspective of digital design engineers away from the register-transfer level to the algorithmic level. It is founded on the capabilities of High-Level Synthesis tools. By consequently working with data type-agnostic source codes, the RDAM brings significant simplifications to the fixed-point conversion of algorithms and the design of complex-valued architectures. Signal processing applications from the field of Compressed Sensing illustrate the efficacy of the RDAM in the context of multi-user wireless communications. For instance, a complex-valued digital architecture of Orthogonal Matching Pursuit with rank-1 updating has successfully been implemented and tested