Abstract:

Battery-powered embedded systems require low energy usage to extend system lifetime. These systems must power many components for long periods of time and are particularly sensitive to energy use. Recent techniques for reducing energy consumption in wireless sensor networks, such as aggregation, require additional computation to reduce energy intensive radio 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 (sensor network nodes). Alternatively, 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. This paper examines the design considerations of a hardware accelerator for compressed Bloom filters, a data structure for efficiently storing set membership. We evaluate our ASHD design for three representative wireless sensor network applications and demonstrate that ASHD design reduces network latency by 59% and computational energy by 98%, showing the need for architecting processors for ASHD accelerators.