Fig. 4: System architecture for edge cloud computing to synchronously convolve 100 clinical ECG signals from patients with CVD.
From: Higher-dimensional processing using a photonic tensor core with continuous-time data
The system has five functional blocks: input light generation and (de)multiplexing in the edge cloud, input-multiplexed RF generation at the edge device and interface, optical modulation relating edge interface and edge cloud, photonic tensor core for in-memory computing in the edge cloud, and output light (de)multiplexing and detection in the edge cloud. In the device layer, each ECG signal is a 1D time-domain signal. In the edge interface layer, the ECG signal data from patient j at time i are denoted as xij and encoded in the amplitude of RF fmod(j,50) using λi or λ′i as the carrier (λi if j ≤ 50; λ′i if j > 50). For j ∈ [1, 100] ⊆ Z+ and i ∈ [1, 3] ⊆ Z+, the input matrix X has dimension d3×100. In the edge cloud layer, the weight bank determined by the photonic tensor core defines a d3×3 matrix W, containing three d1×3 kernels. Effectively, one such matrix–matrix multiplication performs 300 convolutions resulting in a d3×100 matrix Y, which is obtained by convolving the middle three time-domain data of 100 ECG signals using 3 kernels. PD, photodetector; EOM, electro-optic modulator; PC, polarization controller; VOA, variable optical attenuator.
