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Abstract--Spin switch (SS) is a promising spintronic device which exhibits compactness, low power, non-volatility, input-output isolation leveraging giant spin Hall effect, spin transfer torque, and dipolar coupling. In this paper, we propose a novel device-to-architecture co-design for an in-memory computing platform using coterminous SS (IMCS2), which could simultaneously work as non-volatile memory and reconfigurable in-memory logic (AND/NAND, OR/NOR, and XOR/XNOR) without add-on logic circuits to memory chip. The computed logic output could be simply read out like a normal magnetic random access memory bit cell using the shared memory peripheral circuits. Such intrinsic in-memory logic could be used to process data within memory to greatly reduce power-hungry and long distance data communication in the conventional von Neumann computing system. The IMCS2-based in-memory bulk bitwise Boolean vector operation shows ~9x energy saving and ~3x speedup compared with that of DRAM-based in-memory computing platform. We further employ in-memory multiplication to evaluate the performance of the proposed in-memory computing platform for vector-vector multiplication with different vector sizes.

In this paper, we pave a novel way towards the concept of bit-wise In-Memory Convolution Engine (IMCE) that could implement the dominant convolution computation of Deep Convolutional Neural Networks (CNN) within memory. IMCE employs parallel computational memory sub-array as a fundamental unit based on our proposed Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) design. Then, we propose an accelerator system architecture based on IMCE to efficiently process low bit-width CNNs. This architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers and also accelerate CNN inference. The device to architecture co-simulation results show that the proposed system architecture can process low bit-width AlexNet on ImageNet data-set favorably with 785.25μJ/img, which consumes ~3× less energy than that of recent RRAM based counterpart. Besides, the chip area is ~4× smaller.

In this paper we propose a Highly Flexible InMemory (HieIM) computing platform using STT MRAM, which can be leveraged to implement Boolean logic functions without sacrificing memory functionality. It could pre-process data within memory to further reduce power hungry long distance communication between memory and processing units as in Von-Neumann computing system. HieIM can implement all the Boolean logic functions (AND/NAND, OR/NOR, XOR/XNOR) between any two cells in the same memory array, thus overcoming the `operand locality' problem in contemporary in-memory computing platform designs. To investigate the performance of HieIM, we test in-memory bulk bit-wise Boolean logic operations using different vector datasets, which shows ~ 8x energy saving and ~ 5x speedup compared to recent DRAM based in-memory computing platform. We further implement an in-memory data encryption engine design based on HieIM as another case study. With AES algorithm, it shows 51.5% and 68.9% lower energy consumption compared to CMOS-ASIC and CMOL based implementations, respectively.