Magnetic random-access memory (MRAM) is a promising memory technology due to its high density, non-volatility, and high endurance. However, achieving high memory fidelity incurs high write-energy costs, which should be reduced for large-scale deployment of MRAMs. In this paper, we formulate abiconvexoptimization problem to optimize write fidelity given energy and latency constraints. The basic idea is to allocate non-uniform write pulses depending on the importance of each bit position. The fidelity measure we consider is mean squared error (MSE), for which we optimize write pulses via alternating convex search (ACS). We derive analytic solutions and propose analternating water-fillingalgorithm by casting the MRAM’s write operation as communication over parallel channels. Hence, the proposed alternating water-filling algorithm is computationally more efficient than the original ACS while their solutions are identical. Since the formulated biconvex problem is non-convex, both the original ACS and the proposed algorithm do not guarantee global optimality. However, the MSEs obtained by the proposed algorithm are comparable to the MSEs by complicated global nonlinear programming solvers. Furthermore, we prove that our algorithm can reduce the MSE exponentially with the number of bits per word. For an 8-bit accessed word, the proposed algorithm reduces the MSE by a factor of 21. We also evaluate MNIST dataset classification supposing that the model parameters of deep neural networks are stored in MRAMs. The numerical results show that the optimized write pulses can achieve 40% write-energy reduction for the same classification accuracy.

Magnetic random-access memory (MRAM) is a promising memory technology due to its high density, non-volatility, and high endurance. However, achieving high memory fidelity incurs significant write-energy costs, which should be reduced for the large-scale deployment of MRAMs. In this paper, we formulate an optimization problem to maximize the memory fidelity given energy constraints, and propose a biconvex optimization approach to solve it. The basic idea is to allocate non-uniform write pulses depending on the importance of each bit position. We consider the mean squared error (MSE) as a fidelity metric and propose an iterative water-filling algorithm to minimize the MSE. Although the iterative algorithm does not guarantee the global optimality, we can choose a proper starting point that decreases the MSE exponentially and guarantees fast convergence. For an 8-bit accessed word, the proposed algorithm reduces the MSE by a factor of 21.

Several memory vendors are pursuing different kinds of memory cells that can offer high density, non-volatility, high performance, and high endurance. There are several on-going efforts to architect main memory systems with these new NVMs that can compete with traditional DRAM systems. Each NVM has peculiarities that require new microarchitectures and protocols for memory access. In this work, we focus on memristor technology and the sneak currents inherent in memristor crossbar arrays. A read in state-of-the-art designs requires two consecutive reads; the first measures background sneak currents that can be factored out of the current measurement in the second read. This paper introduces a mechanism to reuse the background sneak current measurement for subsequent reads from the same column, thus introducing "open-column" semantics for memristor array access. We also examine a number of data mapping policies that allow the system to balance parallelism and locality. We conclude that on average, it is better to prioritize locality; our best design yields a 20% improvement in read latency and a 26% memory power reduction, relative to the state-of-the-art memristor baseline.