  While the backpropagation of error algorithm enables deep neural network
training, it implies (i) bidirectional synaptic weight transport and (ii)
update locking until the forward and backward passes are completed. Not only do
these constraints preclude biological plausibility, but they also hinder the
development of low-cost adaptive smart sensors at the edge, as they severely
constrain memory accesses and entail buffering overhead. In this work, we show
that the one-hot-encoded labels provided in supervised classification problems,
denoted as targets, can be viewed as a proxy for the error sign. Therefore,
their fixed random projections enable a layerwise feedforward training of the
hidden layers, thus solving the weight transport and update locking problems
while relaxing the computational and memory requirements. Based on these
observations, we propose the direct random target projection (DRTP) algorithm
and demonstrate that it provides a tradeoff between accuracy and computational
cost that is suitable for adaptive edge computing devices.
