  Neuromorphic hardware architectures represent a growing family of potential
post-Moore's Law Era platforms. Largely due to event-driving processing
inspired by the human brain, these computer platforms can offer significant
energy benefits compared to traditional von Neumann processors. Unfortunately
there still remains considerable difficulty in successfully programming,
configuring and deploying neuromorphic systems. We present the Fugu framework
as an answer to this need. Rather than necessitating a developer attain
intricate knowledge of how to program and exploit spiking neural dynamics to
utilize the potential benefits of neuromorphic computing, Fugu is designed to
provide a higher level abstraction as a hardware-independent mechanism for
linking a variety of scalable spiking neural algorithms from a variety of
sources. Individual kernels linked together provide sophisticated processing
through compositionality. Fugu is intended to be suitable for a wide-range of
neuromorphic applications, including machine learning, scientific computing,
and more brain-inspired neural algorithms. Ultimately, we hope the community
adopts this and other open standardization attempts allowing for free exchange
and easy implementations of the ever-growing list of spiking neural algorithms.
