DistriHD: A Memory Efficient Distributed Binary Hyperdimensional Computing Architecture for Image Classification
DistriHD: A Memory Efficient Distributed Binary Hyperdimensional Computing Architecture for Image Classification
复制标题
DistriHD：用于图像分类的内存高效分布式二进制超维计算架构
DOI：
10.1109/asp-dac52403.2022.9712589
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
H. Awano
中科院分区：
文献类型：
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作者：
Dehua Liang;Jun Shiomi;Noriyuki Miura;H. Awano
超维度（HD）计算是一种受大脑启发的学习方法，可在当今的嵌入式设备上实现高效快速的学习。HD计算首先将所有数据点编码为称为超向量的高维向量，然后使用定义良好的操作集有效地执行分类任务。虽然HD计算在几个实际任务中实现了合理的性能，但它具有巨大的内存需求，因为数据点应该存储在具有数千位的非常长的向量中。为了缓解这个问题，我们提出了一种新的HD计算架构，称为DistriHD，它使HD计算能够使用二进制超向量进行训练和测试，并在单遍训练模式下实现高精度，同时具有显著低的硬件资源。DistriHD将数据点编码为分布式二进制超向量，并消除了编码器中昂贵的项目内存，从而显著降低了推理所需的硬件成本。我们的评估还表明，我们的模型可以实现$27.6\times$减少内存成本，而不损害分类精度。硬件实现还表明DistriHD在面积和功耗方面分别实现了超过9.9\times $和28.8\times $的减少。
Hyper-Dimensional (HD) computing is a brain-inspired learning approach for efficient and fast learning on today's embedded devices. HD computing first encodes all data points to high-dimensional vectors called hypervectors and then efficiently performs the classification task using a well-defined set of operations. Although HD computing achieved reasonable performances in several practical tasks, it comes with huge memory requirements since the data point should be stored in a very long vector having thousands of bits. To alleviate this problem, we propose a novel HD computing architecture, called DistriHD which enables HD computing to be trained and tested using binary hypervectors and achieves high accuracy in single-pass training mode with significantly low hardware resources. DistriHD encodes data points to distributed binary hypervectors and eliminates the expensive item memory in the encoder, which significantly reduces the required hardware cost for inference. Our evaluation also shows that our model can achieve a $27.6\times$ reduction in memory cost without hurting the classification accuracy. The hardware implementation also demonstrates that DistriHD achieves over $9.9\times$ and $28.8\times$ reduction in area and power, respectively.
tiny-HD：适用于物联网应用的超高效超维计算引擎
DOI：
10.23919/date51398.2021.9473920
发表时间：
2021
期刊：
and Testing in Europe (DATE
影响因子：
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作者：
Khaleghi, B.
通讯作者：
Khaleghi, B.