Wildlife conservation using continuous monitoring of environmental factors and biomedical classification, which generate a vast amount of sensor data, is a challenge due to limited bandwidth in the case of remote monitoring. It becomes critical to have classification where data is generated, and only classified data is used for monitoring. We present a novel multiplierless framework for in-filter acoustic classification using Margin Propagation (MP) approximation used in low-power edge devices deployable in remote areas with limited connectivity. The entire design of this classification framework is based on template-based kernel machine, which include feature extraction and inference, and uses basic primitives like addition/subtraction, shift, and comparator operations, for hardware implementation. Unlike full precision training methods for traditional classification, we use MP-based approximation for training, including backpropagation mitigating approximation errors. The proposed framework is general enough for acoustic classification. However, we demonstrate the hardware friendliness of this framework by implementing a parallel Finite Impulse Response (FIR) filter bank in a kernel machine classifier optimized for a Field Programmable Gate Array (FPGA). The FIR filter acts as the feature extractor and non-linear kernel for the kernel machine implemented using MP approximation and a downsampling method to reduce the order of the filters. The FPGA implementation on Spartan 7 shows that the MP-approximated in-filter kernel machine is more efficient than traditional classification frameworks with just less than 1K slices.

翻译：通过持续监测环境因素和生物医学分类实现野生动物保护会产生大量传感器数据，由于远程监测场景下带宽受限，这成为一大挑战。因此，在数据生成端进行实时分类，仅将分类结果用于监测变得至关重要。我们提出了一种基于边界传播（MP）近似的无乘法器框架，用于实现滤波器内声学分类，该框架可部署在偏远地区低功耗边缘设备上，且无需依赖稳定网络连接。该分类框架整体基于模板化核机器架构，包含特征提取与推理模块，硬件实现仅需加法/减法、移位和比较器等基本运算单元。与传统分类的全精度训练方法不同，我们采用基于MP的近似训练方法，包括通过反向传播缓解近似误差。该框架具有通用性，适用于声学分类任务。我们通过在现场可编程门阵列（FPGA）上优化实现的并行有限脉冲响应（FIR）滤波器组核机器分类器，验证了该框架的硬件友好性。FIR滤波器同时承担特征提取器和非线性核功能，通过MP近似与降采样方法降低滤波器阶数。Spartan 7 FPGA实现结果表明，MP近似的滤波器内核机器比传统分类框架更高效，仅消耗不到1K片资源。