Abstract
High-resolution sensors are critical for robust autonomous perception but impose a severe memory wall on battery-constrained electric vehicles. In these systems, data movement energy often outweighs computation. Traditional image compression is ill-suited as it is semantically blind and optimizes for storage rather than bus switching activity. We propose MotiMem, a hardware-software co-designed interface. Exploiting temporal coherence,MotiMem uses lightweight 2D Motion Propagation to dynamically identify Regions of Interest (RoI). Complementing this, a Hybrid Sparsity-Aware Coding scheme leverages adaptive inversion and truncation to induce bitlevel sparsity. Extensive experiments across nuScenes, Waymo, and KITTI with 16 detection models demonstrate that MotiMem reduces memory-interface dynamic energy by approximately 43 percent while retaining approximately 93 percent of the object detection accuracy, establishing a new Pareto frontier significantly superior to standard codecs like JPEG and WebP.
Abstract (translated)
高分辨率传感器对于实现鲁棒的自主感知至关重要，但给电池约束型电动车带来了严重的内存墙。在这些系统中，数据移动能耗往往超过计算能耗。传统图像压缩因其语义盲特性且优化目标为存储而非总线切换活动，并不适用。我们提出了 MotiMem，一个硬件-软件协同设计的接口。利用时间相干性，MotiMem 采用轻量级二维运动传播动态识别感兴趣区域（RoI）。作为补充，一种混合稀疏感知编码方案通过自适应求逆与截断来诱导比特级稀疏性。在 nuScenes、Waymo 和 KITTI 数据集上使用 16 个检测模型的广泛实验表明，MotiMem 在保留约 93% 目标检测精度的同时，将内存接口动态能耗降低了约 43%，建立了显著优于 JPEG 和 WebP 等标准编解码器的新帕累托前沿。
URL
https://arxiv.org/abs/2603.27108
