Although it has been surpassed by many subsequent coding standards, JPEG occupies a large share of the storage load of the current data hosting service. To reduce the storage costs, DropBox proposed a lossless secondary compression algorithm, Lepton, to further improve the compression rate of JPEG images. However, the bloated probability models defined by Lepton severely restrict its throughput and energy efficiency. To solve this problem, we construct an efficient access probability-based hash function for the probability models, and then propose a hardware-friendly memory optimization method by combining the proposed hash function and the N-way Set-Associative unit. After that, we design a highly parameterized hardware structure for the probability models and finally implement a power and area efficient Lepton hardware encoder. To the best of our knowledge, this is the first hardware implementation of Lepton. The synthesis result shows that the proposed hardware structure reduces the total area of the probability models by 70.97%. Compared with DropBox's software solution, the throughput and the energy efficiency of the proposed Lepton hardware encoder are increased by 55.25 and 4899 times respectively. In terms of manufacturing cost, the proposed Lepton hardware encoder is also significantly lower than the general-purpose CPU used by DropBox.

中文翻译：

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具有基于哈希的内存优化的功率和面积高效的Lepton硬件编码器

尽管已被许多后续的编码标准所超越，但JPEG占据了当前数据托管服务的很大一部分存储负载。为了降低存储成本，DropBox提出了无损二次压缩算法Lepton，以进一步提高JPEG图像的压缩率。但是，Lepton定义的膨胀概率模型严重限制了其吞吐量和能效。为了解决这个问题，我们为概率模型构造了一个基于访问概率的有效哈希函数，然后通过将所提出的哈希函数和N-way-Set-Associative单元相结合，提出了一种硬件友好的内存优化方法。之后，我们为概率模型设计了高度参数化的硬件结构，并最终实现了功率和面积高效的Lepton硬件编码器。据我们所知，这是Lepton的第一个硬件实现。综合结果表明，所提出的硬件结构使概率模型的总面积减少了70.97％。与DropBox的软件解决方案相比，所提出的Lepton硬件编码器的吞吐量和能效分别提高了55.25和4899倍。就制造成本而言，建议的Lepton硬件编码器也大大低于DropBox使用的通用CPU。所提出的Lepton硬件编码器的吞吐量和能效分别提高了55.25和4899倍。就制造成本而言，建议的Lepton硬件编码器也大大低于DropBox使用的通用CPU。所提出的Lepton硬件编码器的吞吐量和能效分别提高了55.25和4899倍。就制造成本而言，建议的Lepton硬件编码器也大大低于DropBox使用的通用CPU。