用稀疏超维计算提升脑电波癫痫检测能效，降低植入设备功耗。
iEEG Seizure Detection with a Sparse Hyperdimensional Computing Accelerator
- 设计压缩项内存与简化空间聚合，优化稀疏超维计算硬件
- 相比稠密实现，能效提升1.73倍，面积效率提升2.20倍
- 适合低功耗脑机接口芯片研发，尤其关注续航的植入设备
用于可靠颅内脑电图（iEEG）监测的可植入设备需要高效、准确且实时的癫痫发作检测。稠密超维计算（HDC）在性能上优于神经网络，但在超低功耗场景下仍存在显著开关功耗问题。稀疏超维计算有望进一步降低能耗，但需支持更复杂操作并引入额外超参数——最大超向量密度。为提升稀疏HDC的能效与面积效率，本文提出压缩项内存（CompIM）并简化空间捆绑机制。分析表明，合理选择超参数可显著降低检测延迟。最终，所提优化使硬件设计能效提升1.73倍、面积效率提升2.20倍，相较原始稀疏实现；同时比稠密实现能效高7.50倍、面积效率高3.24倍。该工作凸显了稀疏HDC的硬件优势，证明其可实现体积更小、续航更长的脑植入设备，优于当前最先进方案。
原文摘要 · Abstract (English)
Implantable devices for reliable intracranial electroencephalography (iEEG) require efficient, accurate, and real-time detection of seizures. Dense hyperdimensional computing (HDC) proves to be efficient over neural networks; however, it still consumes considerable switching power for an ultra-low energy application. Sparse HDC, on the other hand, has the potential of further reducing the energy consumption, yet at the expense of having to support more complex operations and introducing an extra hyperparameter, the maximum hypervector density. To improve the energy and area efficiency of the sparse HDC operations, this work introduces the compressed item memory (CompIM) and simplifies the spatial bundling. We also analyze how a proper hyperparameter choice improves the detection delay compared to dense HDC. Ultimately, our optimizations achieve a 1.73x more energy- and 2.20x more area-efficient hardware design than the naive sparse implementation. We are also 7.50x more energy- and 3.24x more area-efficient than the dense HDC implementation. This work highlights the hardware advantages of sparse HDC, demonstrating its potential to enable smaller brain implants with a substantially extended battery life compared to the current state-of-the-art.
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