In the hardware implementation of deep learning algorithms such as Convolutional Neural Networks (CNNs), vector-vector multiplications and memories for storing parameters take a significant portion of area and power consumption. In this paper, we propose a Domain Wall Memory (DWM) based design of CNN convolutional layer. In the proposed design, the resistive cell sensing mechanism is efficiently exploited to design a low-cost DWM-based cell arrays for storing parameters. The unique serial access mechanism and small footprint of DWM are also used to reduce the area and power cost of the input registers for aligning inputs. Contrary to the conventional implementation using Memristor-Based Crossbar (MBC), the bit-width of the proposed CNN convolutional layer is extendable for high resolution classifications and training. Simulation results using 65 nm CMOS process show that the proposed design archives 34% of energy savings compared to the conventional MBC based design approach.

In many Digital Signal Processing (DSP) applications such as Viterbi decoder and Fast Fourier Transform (FFT), Static Random Access Memory (SRAM) based embedded memory consumes significant portion of area and power. These DSP units are dominated by sequential memory access where SRAM-based memory is inefficient in terms of area and power. We propose spintronic Domain Wall Memory (DWM) based embedded memories for DSP building blocks e.g., survivor-path memories in Viterbi decoder and First-In-First-Out (FIFO) register files in FFT processor that exploit the unique serial access mechanism, non-volatility and small footprint of the memory for area and power saving. Simulations using 65nm technology show that the DWM based Viterbi decoder achieves 66.4 % area and 59.6 % power savings over the conventional SRAM-based implementation. For 8K point FFT processor, the DWM based design shows 60.6 % area and 60.3 % power savings.