Abstract:

Battery-powered embedded systems, such as wireless sensor network (WSN) motes, require low energy usage to extend system lifetime. WSN motes must power sensors, a processor, and a radio for wireless communication over long periods of time, and are therefore particularly sensitive to energy use. Recent techniques for reducing WSN energy consumption, such as aggregation, require additional computation to reduce the cost of sending data by minimizing radio data transmissions. Larger demands on the processor will require more computational energy, but traditional energy reduction approaches, such as multi-core scaling with reduced frequency and voltage may prove heavy handed and ineffective for motes. Instead, application-specific hardware design (ASHD) architectures can reduce computational energy consumption by processing operations common to specific applications more efficiently than a general purpose processor. By the nature of their deeply embedded operation, motes support a limited set of applications, and thus the conventional general purpose computing paradigm may not be well-suited to mote operation. Both simple and complex operations can improve performance and use orders of magnitude less energy with application-specific hardware. This paper examines the design considerations of a hardware accelerator for compressed Bloom filters, a data structure for efficiently storing set membership. Additionally, we evaluate our ASHD design for three representative wireless sensor network applications: monitoring network-wide mote status, object tracking, and on-mote duplicate packet filtering. We demonstrate that ASHD design reduces network latency by 59% and computational energy by 98%, and show the need for architecting processors for ASHD accelerators.