A Non-Volatile All-Spin Analog Matrix Multiplier: An Efficient Hardware Accelerator for Machine Learning

We describe a novel non-volatile nanomagnetic analog matrix multiplier performing the multiply-and-accumulate (MAC) operation using two magnetic tunnel junctions – one activated by strain to act as the multiplier, and the other activated by spin-orbit torque pulses to act as a domain wall synapse for the accumulation operation. Each MAC operation takes  ~1 ns and dissipates no more than  ~100 aJ of energy. This provides a very useful hardware accelerator for machine learning (e.g. training of deep neural networks), solving combinatorial optimization problems with Ising type machines, and other artificial intelligence tasks which mostly involve the multiplication of large matrices. The non-volatility allows the matrix multiplier to enable non-von-Neumann architectures. It also allows all computing to be done at the edge while reducing the need to access the cloud, thereby making artificial intelligence systems employing this matrix multiplier extremely resilient against cyberattacks.