An evolvable artificial neural network platform for dynamic environments

Dedicated hardware implementations of artificial neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, custom hardware implementations do not support intrinsic training of these networks on-chip. Training is typically done using software simulations and the obtained network is synthesized and targeted to hardware offline. The ANN FPGA design presented here facilitates dynamic network structure and parameter changes required for intrinsic training of artificial neural networks, without reliance on runtime FPGA reconfigurations. This is an important feature for an online trainable system as typical reconfiguration cycle times on the order of a few milliseconds pose to be a major performance bottleneck for iterative training algorithms. The designed platform implements block-based neural networks and can be evolved and adapted in-field.