The current developments in the research area of in-memory computing and analog processing for neural networks and data analysis are significantly advancing the field. There is a clear trend towards leveraging novel memory technologies, such as phase change memory (PCM) and resistive random access memory (ReRAM), to enhance both energy efficiency and computational speed. These advancements are particularly focused on optimizing hardware-software co-design, with a strong emphasis on integrating analog processing capabilities directly within memory arrays to bypass traditional bottlenecks. The integration of deep learning techniques with these novel memory architectures is also a key area of innovation, aiming to mitigate the inherent challenges of analog computation, such as noise and accuracy degradation. Additionally, there is a growing interest in developing open-source simulation frameworks to standardize the evaluation of these new technologies, ensuring transparency and reproducibility in research. Overall, the field is moving towards more efficient, scalable, and reliable solutions for deploying complex neural network models on edge devices and for handling large-scale data analysis tasks.

Noteworthy papers include one that introduces a low-power PCM-based accelerator for mass spectrometry analysis, achieving significant improvements in energy and delay efficiency. Another paper presents a co-design framework for efficient transformers on edge systems, demonstrating substantial system-wide speedups with minimal impact on quality of service. A third paper highlights the need for high design standards in SRAM-based analog compute-in-memory, proposing solutions to improve inference quality while maintaining energy efficiency.