Task-oriented object detection (TOOD) atop CLIP offers open-vocabulary, prompt-driven semantics, yet dense per-window computation and heavy memory traffic hinder real-time, power-limited edge deployment. We present \emph{TorR}, a brain-inspired \textbf{algorithm--architecture co-design} that \textbf{replaces CLIP-style dense alignment with a hyperdimensional (HDC) associative reasoner} and turns temporal coherence into reuse. On the \emph{algorithm} side, TorR reformulates alignment as HDC similarity and graph composition, introducing \emph{partial-similarity reuse} via (i) query caching with per-class score accumulation, (ii) exact $δ$-updates when only a small set of hypervector bits change, and (iii) similarity/load-gated bypass under high system load. On the \emph{architecture} side, TorR instantiates a lane-scalable, bit-sliced item memory with bank/precision gating and a lightweight controller that schedules bypass/$δ$/full paths to meet RT-30/RT-60 targets as object counts vary. Synthesized in a TSMC 28\,nm process and exercised with a cycle-accurate simulator, TorR sustains real-time throughput with millijoule-scale energy per window ($\approx$50\,mJ at 60\,FPS; $\approx$113\,mJ at 30\,FPS) and low latency jitter, while delivering competitive [email protected] across five task prompts (mean 44.27\%) within a bounded margin to strong VLM baselines, but at orders-of-magnitude lower energy. The design exposes deployment-time configurability (effective dimension $D'$, thresholds, precision) to trade accuracy, latency, and energy for edge budgets.

翻译：任务导向目标检测（TOOD）基于CLIP模型可实现开放词汇与提示驱动语义理解，但密集的逐窗口计算与大量内存访问阻碍了实时、低功耗边缘部署。本文提出**TorR**——一种受脑启发的**算法-架构协同设计**，通过**超维度（HDC）关联推理器替代CLIP式密集对齐**，并将时序相关性转化为计算复用。在**算法层面**，TorR将对齐重构为HDC相似度计算与图组合操作，引入**部分相似度复用机制**：通过（i）带有逐类分数累积的查询缓存，（ii）当超向量中仅有少量比特变化时执行精确的$δ$更新，以及（iii）在高系统负载下基于相似度/负载门控的旁路机制。在**架构层面**，TorR实现了面向通道的可扩展位切片式条目存储器（配备bank/精度门控），并通过轻量级控制器根据目标数量动态调度旁路/$δ$/全计算路径，以满足RT-30/RT-60实时约束。基于TSMC 28nm工艺综合验证，结合周期精确仿真器测试显示，TorR可在毫焦级功耗下（60FPS时约50mJ，30FPS时约113mJ）维持实时吞吐量，兼具低延迟抖动，同时在五项任务提示中（平均[email protected]为44.27%）以有限精度损失与强视觉语言模型基线竞争，但能耗降低数个数量级。该设计提供部署时可配置性（有效维度$D'$、阈值、精度），支持在边缘预算约束下权衡精度、延迟与能耗。