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 pro-
cessor, 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 com-
putation to reduce the cost of sending data by minimizing
radio data transmissions. Larger demands on the processor
will require more computational energy, but traditional en-
ergy 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 en-
ergy consumption by processing common operations to spe-
cific applications more efficiently than a general purpose pro-
cessor. 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 com-
plex operations can use orders of magnitude less energy with
application-specific hardware. Simple operations, such as
bit-level manipulations which poorly utilize a general pur-
pose processor, can be processed in parallel with custom
hardware. Complex operations, requiring many operations
on general purpose processors, require fewer cycles in custom
hardware. By spending computational energy only on the
operations needed by the application, application-specific
hardware improves energy use and performance. This paper
examines the design considerations of a hardware accelera-
tor for 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 compu-
tational energy by 98%, and show the need for architecting
processors for ASHD accelerators.