Nair, AR and Nath, PK and Chakrabartty, S and Thakur, CS (2024) Multiplierless In-filter Computing for tinyML Platforms. In: 37th International Conference on VLSI Design, VLSID 2024, 6 January 2024through 10 January 2024, Kolkata, West Bengal, pp. 192-197.
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Abstract
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. 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 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. 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. © 2024 IEEE.
| Item Type: | Conference Paper |
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| Publication: | Proceedings of the IEEE International Conference on VLSI Design |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to authors. |
| Keywords: | Conservation; Edge computing; Field programmable gate arrays (FPGA); Impulse response; Internet of things, Acoustic classification; Classification framework; Edge computing; Field programmable gate array; Field programmables; IoT; Kernel machine; Multiplierless; Programmable gate array; Wildlife conservation, FIR filters |
| Department/Centre: | Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) |
| Date Deposited: | 28 Aug 2024 11:52 |
| Last Modified: | 28 Aug 2024 11:52 |
| URI: | http://eprints.iisc.ac.in/id/eprint/84866 |
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