Towards Low-Cost, Real-Time, Distributed Signal and Data Processing for Artificial Intelligence Applications at Edges of Large Industrial and Internet Networks

Digital Signal Processors (DSP) are vital system components in industrial Artificial Intelligence (AI) applications. In this paper, FIR filters that could be used for industrial AI applications are designed from the Spline Biorthogonal 1.5 (SB1.5) mother wavelet using a real-time, low-cost, generic industrial IoT (IIoT) hardware: the C28x DSP and low-level, Embedded C, system software. Our contribution in this paper is the first reported application of the C28x for SB1.5 wavelet construction. The significance of this approach is to be able to repurpose low-cost, readily available hardware for distributed AI applications. Our approach is different from the state of the art, in which specialized hardware are always manufactured for implementing AI applications at large network edges. Our approach supports low-cost and fast single-stage FIR implementation suitable for use in real-time, distributed AI application at network edges, since in our case, successive recursion of FIR filters leading to a full implementation of Pyramid Algorithm is not implemented. The designed FIR filter is evaluated and found capable of both low-pass and high pass filtering operations. Results of this paper indicate that the C28x real-time DSP, which exists in many IoT devices, could have improved scalability by being deployed for other important AI and IoT network edge analytic applications, different from its present uses.