Seculator：一种快速安全的神经处理单元
Seculator: A Fast and Secure Neural Processing Unit
摘要：
保护深度神经网络（DNN）是一个非常重要的问题，因为ML模型包含了高质量的知识产权，由机械土耳其人精心整理的数据集的特征，以及在大型集群计算机上训练的新方法。可悲的是，提取模型参数的攻击正在增加，因此设计人员被迫创建用于保护此类模型的架构。该领域中的现有技术的提议将这样的网络的确定性存储器访问模式纳入认知（尽管是部分地），将一组存储器块分组为瓦片，并且在瓦片级别维持状态（以减少存储空间）。为了提供完整性保证（防止篡改），他们没有提出任何重要的优化，仍然保持块级状态。我们观察到，有可能进一步利用DNN的确定性存储器访问模式，并且仅维护当前区块和当前层的状态信息，当前区块和当前层可以包括大量区块。这减少了存储空间，减少了存储器访问的次数，提高了性能，并简化了设计，而不会牺牲任何安全保证。在我们提出的加速器架构，Seculator的关键技术，是编码内存访问模式，以创建一个小的基于硬件的瓦片版本号生成器为给定的层，并存储层级MAC。我们完全消除了对MAC缓存和瓦片版本号存储的需要（如在相关工作中所使用的）。我们表明，使用智能设计的数学运算，这些结构是不需要的。通过减少这样的开销，我们表现出16%的速度比最接近的竞争工作。
英文摘要：
Securing deep neural networks (DNNs) is a problem of significant interest since an ML model incorporates high-quality intellectual property, features of data sets painstakingly collated by mechanical turks, and novel methods of training on large cluster computers. Sadly, attacks to extract model parameters are on the rise, and thus designers are being forced to create architectures for securing such models. State-of-the-art proposals in this field take the deterministic memory access patterns of such networks into cognizance (albeit partially), group a set of memory blocks into a tile, and maintain state at the level of tiles (to reduce storage space). For providing integrity guarantees (tamper avoidance), they don't propose any significant optimizations, and still maintain block-level state. We observe that it is possible to exploit the deterministic memory access patterns of DNNs even further, and maintain state information for only the current tile and current layer, which may comprise a large number of tiles. This reduces the storage space, reduces the number of memory accesses, increases performance, and simplifies the design without sacrificing any security guarantees. The key techniques in our proposed accelerator architecture, Seculator, are to encode memory access patterns to create a small HW-based tile version number generator for a given layer, and to store layer-level MACs. We completely eliminate the need for having a MAC cache and a tile version number store (as used in related work). We show that using intelligently-designed mathematical operations, these structures are not required. By reducing such overheads, we show a speedup of 16% over the closest competing work.
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