Abstract
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.
Abstract (translated)
人类通过将稀疏观测整合到密集连接的神经元中,构建了我们对空间的感知,这使得人工智能在医学成像、增强现实(AR)和 embodied AI等领域具有卓越的并行度和效率。在AI中实现这种能力面临着软件和硬件方面的挑战。在软件方面,困难源于传统显式信号表示中存储效率低下。硬件方面,包括由冯·诺伊曼瓶颈限制了CPU和内存之间的数据传输,以及CMOS电路在支持并行处理方面的限制。我们提出了一个软件和硬件协同优化的信号重构系统,可以从稀疏输入中恢复信号。在软件方面,我们使用神经场通过神经网络隐含表示信号,并使用低秩分解和结构化剪裁进一步压缩。在硬件方面,我们设计了一个基于电阻性内存的计算在内存(CIM)平台,包括一个高斯编码器(GE)和一个多层感知器(MLP处理引擎(PE)。GE利用电阻性内存的固有随机性实现高效的输入编码,而PE通过硬件感知量化(HAQ)电路实现精确的权重映射。我们在基于40nm的256Kb电阻性内存的内存计算宏观上展示了系统的效果,实现了巨大的能效和并行度改进,而不会牺牲重构质量,例如3D CT稀疏重建、新颖视图合成和动态场景下的新颖视图合成。这项工作推动了AI驱动的信号修复技术的发展,为未来的高效和可靠的医疗AI和3D视觉应用铺平了道路。
URL
https://arxiv.org/abs/2404.09613
