Edge computing is a promising solution for handling high-dimensional,
multispectral analog data from sensors and IoT devices for applications such as
autonomous drones. However, edge devices' limited storage and computing
resources make it challenging to perform complex predictive modeling at the
edge. Compute-in-memory (CiM) has emerged as a principal paradigm to minimize
energy for deep learning-based inference at the edge. Nevertheless, integrating
storage and processing complicates memory cells and/or memory peripherals,
essentially trading off area efficiency for energy efficiency. This paper
proposes a novel solution to improve area efficiency in deep learning inference
tasks. The proposed method employs two key strategies. Firstly, a Frequency
domain learning approach uses binarized Walsh-Hadamard Transforms, reducing the
necessary parameters for DNN (by 87% in MobileNetV2) and enabling
compute-in-SRAM, which better utilizes parallelism during inference. Secondly,
a memory-immersed collaborative digitization method is described among CiM
arrays to reduce the area overheads of conventional ADCs. This facilitates more
CiM arrays in limited footprint designs, leading to better parallelism and
reduced external memory accesses. Different networking configurations are
explored, where Flash, SA, and their hybrid digitization steps can be
implemented using the memory-immersed scheme. The results are demonstrated
using a 65 nm CMOS test chip, exhibiting significant area and energy savings
compared to a 40 nm-node 5-bit SAR ADC and 5-bit Flash ADC. By processing
analog data more efficiently, it is possible to selectively retain valuable
data from sensors and alleviate the challenges posed by the analog data deluge.

通过使用频域学习方法和内存沉浸式协同数字化方法，该研究提出了一种改善深度学习推断任务中的面积效率的新方法，在处理来自传感器和物联网设备的高维多光谱模拟数据方面具有潜在的应用前景。

通过协同计算内存网络中的频域压缩在边缘处控制模拟数据洪水

Containing Analog Data Deluge at Edge through Frequency-Domain  Compression in Collaborative Compute-in-Memory Networks

To accommodate structured approaches of neural computation, we propose a
class of recurrent neural networks for indexing and storing sequences of
symbols or analog data vectors. These networks with randomized input weights
and orthogonal recurrent weights implement coding principles previously
described in vector symbolic architectures (VSA), and leverage properties of
reservoir computing. In general, the storage in reservoir computing is lossy
and crosstalk noise limits the retrieval accuracy and information capacity. A
novel theory to optimize memory performance in such networks is presented and
compared with simulation experiments. The theory describes linear readout of
analog data, and readout with winner-take-all error correction of symbolic data
as proposed in VSA models. We find that diverse VSA models from the literature
have universal performance properties, which are superior to what previous
analyses predicted. Further, we propose novel VSA models with the statistically
optimal Wiener filter in the readout that exhibit much higher information
capacity, in particular for storing analog data.
The presented theory also applies to memory buffers, networks with gradual
forgetting, which can operate on infinite data streams without memory overflow.
Interestingly, we find that different forgetting mechanisms, such as
attenuating recurrent weights or neural nonlinearities, produce very similar
behavior if the forgetting time constants are aligned. Such models exhibit
extensive capacity when their forgetting time constant is optimized for given
noise conditions and network size. These results enable the design of new types
of VSA models for the online processing of data streams.

本文提出了一类用于索引和存储符号序列或模拟数据向量的递归神经网络，利用储池计算的性质，采用随机输入权重和正交递归权重实现之前在向量符号体系结构（VSA）中描述的编码原理，并提出了优化语音缓存网络，使其能够在处理数据流时在线工作的新型 VSA 模型的设计。