Detecting broad peaks in sRNA-seq data
The sure why any software should be confined to only broad peaks, but there is two packages that should do what I thing you have in mind. Just to clarify, for smallRNAs peak detection is sometimes also referred to as loci or cluster detection:


ShortStack which is developed for alignment, annotation, and quantification of small RNAs. The de-novo cluster finding option as copied from the manual might do the trick:


DE-NOVO CLUSTER FINDING
    Unless options --locus or --locifile are used (see below), ShortStack
    will de-novo identify clusters of small RNA accumulation genome-wide.
    Cluster definition is simple: First, all regions containing at least one
    primary alignment are found where the maximum distance between the ends
    of the alignments is &lt;= option --pad (default: 75). Second, if the
    number of alignments in the cluster is &gt;= option --mincov (default:
    0.5rpm), the cluster is kept. The mincov threshold can also be specified
    in terms of reads per million by using a value such as 3.2rpm (which
    specifies the threshold to be 3.2 reads per million). Using a rpm
    threshold allows the sensitivity of cluster discovery to be normalized
    between libraries of different sizes. Alternatively, reads per million
    mapped (rpmm) can be specified: A --mincov of 1.2rpmm indicates 1.2
    reads per million mapped is the threshold. rpm is a fraction of total
    library size, while rpmm is a fraction of only the aligned &amp; placed
    fraction of the library.



segmentSeq
is an R-package useing a Bayesian approach for small RNA locus detection. It's however really slow and not developed any further by the main author.