Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, traditional signal reconstruction methods on digital computers face both software and hardware challenges. On the software front, difficulties arise from storage inefficiencies in conventional explicit signal representation. Hardware obstacles include the von Neumann bottleneck, which limits data transfer between the CPU and memory, and the limitations of CMOS circuits in supporting parallel processing. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. Software-wise, we employ neural field to implicitly represent signals via neural networks, which is further compressed using low-rank decomposition and structured pruning. Hardware-wise, we design a resistive memory-based computing-in-memory (CIM) platform, featuring a Gaussian Encoder (GE) and an MLP Processing Engine (PE). The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit. We demonstrate the system's efficacy on a 40nm 256Kb resistive memory-based in-memory computing macro, achieving huge energy efficiency and parallelism improvements without compromising reconstruction quality in tasks like 3D CT sparse reconstruction, novel view synthesis, and novel view synthesis for dynamic scenes. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications.

翻译：人类通过将稀疏观测整合到大规模互联突触及神经元中构建空间感知，展现出卓越的并行性与效率。在人工智能领域复现这一能力，可为医学成像、增强现实/虚拟现实（AR/VR）及具身智能等输入数据稀疏且计算资源受限的应用场景提供广泛支持。然而，传统数字计算机上的信号重建方法面临软硬件双重挑战：在软件层面，常规显式信号表示存在存储效率低下问题；硬件障碍则包括限制中央处理器与存储器间数据传输的冯·诺依曼瓶颈，以及互补金属氧化物半导体（CMOS）电路在支持并行处理方面的局限性。我们提出一种软硬件协同优化的系统级方法，用于从稀疏输入信号中实现重建。在软件方面，采用神经场通过神经网络隐式表示信号，并进一步通过低秩分解与结构化剪枝技术进行压缩；在硬件方面，设计基于阻变存储器的存算一体（CIM）平台，该平台包含高斯编码器（GE）与多层感知机处理引擎（PE）。其中，高斯编码器利用阻变存储器的固有随机性实现高效输入编码，处理引擎则通过硬件感知量化（HAQ）电路实现精确权重映射。我们在基于40纳米256Kb阻变存储器的存内计算宏单元上验证了系统效能，在三维计算机断层扫描（CT）稀疏重建、新视角合成及动态场景新视角合成等任务中，实现了能效与并行性的显著提升，且未牺牲重建质量。该工作推动了基于人工智能的信号恢复技术发展，为未来高效鲁棒的医学人工智能及三维视觉应用奠定基础。