This work investigates the role of the emerging Analog In-memory computing
(AIMC) paradigm in enabling Medical AI analysis and improving the certainty of
these models at the edge. It contrasts AIMC's efficiency with traditional
digital computing's limitations in power, speed, and scalability. Our
comprehensive evaluation focuses on brain tumor analysis, spleen segmentation,
and nuclei detection. The study highlights the superior robustness of isotropic
architectures, which exhibit a minimal accuracy drop (0.04) in analog-aware
training, compared to significant drops (up to 0.15) in pyramidal structures.
Additionally, the paper emphasizes IMC's effective data pipelining, reducing
latency and increasing throughput as well as the exploitation of inherent noise
within AIMC, strategically harnessed to augment model certainty.

该研究探讨了新兴的模拟内存计算（AIMC）在医疗人工智能分析中的作用，并改善边缘计算中模型的准确性。通过对比 AIMC 的效率与传统数字计算的局限性，对脑肿瘤分析、脾脏分割和核分割进行了全面评估。研究重点突出了各向同性架构的出色鲁棒性，在模拟感知训练中仅出现最小的准确度下降（0.04），而棱锥体结构则出现显著下降（高达 0.15）。此外，该论文强调了 IMC 的有效数据流水线处理，减少延迟，增加吞吐量，并巧妙地利用 AIMC 内在的噪音来增强模型的确定性。