  In-memory computing with resistive crossbar arrays has been suggested to
accelerate deep-learning workloads in highly efficient manner. To unleash the
full potential of in-memory computing, it is desirable to accelerate the
training as well as inference for large deep neural networks (DNNs). In the
past, specialized in-memory training algorithms have been proposed that not
only accelerate the forward and backward passes, but also establish tricks to
update the weight in-memory and in parallel. However, the state-of-the-art
algorithm (Tiki-Taka version 2 (TTv2)) still requires near perfect offset
correction and suffers from potential biases that might occur due to
programming and estimation inaccuracies, as well as longer-term instabilities
of the device materials. Here we propose and describe two new and improved
algorithms for in-memory computing (Chopped-TTv2 (c-TTv2) and Analog Gradient
Accumulation with Dynamic reference (AGAD)), that retain the same runtime
complexity but correct for any remaining offsets using choppers. These
algorithms greatly relax the device requirements and thus expanding the scope
of possible materials potentially employed for such fast in-memory DNN
training.
