  The fundamental problem considered in this paper is "What is the
\textit{energy} consumed for the implementation of a \emph{compressive sensing}
decoding algorithm on a circuit?". Using the "information-friction" framework,
we examine the smallest amount of \textit{bit-meters} as a measure for the
energy consumed by a circuit. We derive a fundamental lower bound for the
implementation of compressive sensing decoding algorithms on a circuit. In the
setting where the number of measurements scales linearly with the sparsity and
the sparsity is sub-linear with the length of the signal, we show that the
\textit{bit-meters} consumption for these algorithms is order-tight, i.e., it
matches the lower bound asymptotically up to a constant factor. Our
implementations yield interesting insights into design of energy-efficient
circuits that are not captured by the notion of computational efficiency alone.
