Neuromorphic Denoising with Fully Analog Memristive In‐Memory Computing

Analog compute‐in‐memory (CIM) technology utilizes the physical characteristics of memory devices to cancel the repeated data movement between memory and processors. Since this method gets rid of the signal domain conversion, analog CIM is suitable for the post‐sensing processing systems. However, two critical challenges emerge: a lack of area‐efficient analog buffers to complete the analog computing flow and the inevitable accumulation of noise throughout the continuous signal path. Previous works either incur extra power and space costs to mitigate these issues or compromise the analog CIM concept by introducing digital circuits. In this article, we borrow the concepts of episodic memory in human brains to experimentally implement a memristor‐based neuromorphic denoising process. We experimentally demonstrate a homogeneous memristor processing unit for both temporal storage and neural network computation, imitating the synapses in the human brain. Furthermore, based on previous research on functional modeling of episodic memory, we experimentally demonstrate an analog neuromorphic denoising system utilizing the proposed homogeneous memristor cores. Thanks to this neuromorphic design, compared with the latest analog computing neural network works, the proposed method improves the power efficiency by times, saves overall on‐chip analog buffer by times, and achieves area efficiency improvement.