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1. MnM: A Fast and Efficient Min/Max Searching in MRAM

   https://doi.org/10.1145/3526241.3530349  

   Sridharan, Amitesh; Zhang, Fan; Fan, Deliang (June 2022, Great Lakes Symposium on VLSI)

   In-Memory Computing (IMC) technology has been considered to be a promising approach to solve well-known memory-wall challenge for data intensive applications. In this paper, we are the first to propose MnM, a novel IMC system with innovative architecture/circuit designs for fast and efficient Min/Max searching computation in emerging Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM). Our proposed SOT-MRAM based in-memory logic circuits are specially optimized to perform parallel, one-cycle XNOR logic that are heavily used in the Min/Max searching-in-memory algorithm. Our novel in-memory XNOR circuit also has an overhead of just two transistors per row when compared to most prior methodologies which typically use multiple sense amplifiers or complex CMOS logic gates. We also design all other required peripheral circuits for implementing complete Min/Max searching-in-MRAM computation. Our cross-layer comprehensive experiments on Dijkstra's algorithm and other sorting algorithms in real word datasets show that our MnM could achieve significant performance improvement over CPUs, GPUs, and other competing IMC platforms based on RRAM/MRAM/DRAM. 

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2. Energy Efficient In-Memory Binary Deep Neural Network Accelerator with Dual-Mode SOT-MRAM

   https://doi.org/10.1109/ICCD.2017.107  

   Fan, Deliang; Angizi, Shaahin (November 2017, 2017 IEEE International Conference on Computer Design (ICCD))

   In this paper, we explore potentials of leveraging spin-based in-memory computing platform as an accelerator for Binary Convolutional Neural Networks (BCNN). Such platform can implement the dominant convolution computation based on presented Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array. The proposed array architecture could simultaneously work as non-volatile memory and a reconfigurable in-memory logic (AND, OR) without add-on logic circuits to memory chip as in conventional logic-in-memory designs. The computed logic output could be also simply read out like a normal MRAM bit-cell using the shared memory peripheral circuits. We employ such intrinsic in-memory computing architecture to efficiently process data within memory to greatly reduce power hungry and omit long distance data communication concerning state-of-the-art BCNN hardware. 

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3. Embedding Binary Perceptrons in FPGA to improve Area, Power and Performance

   https://doi.org/10.1109/ICCAD45719.2019.8942071  

   Wagle, Ankit; Azari, Elham; Vrudhula, Sarma (November 2019, International Conference on Computer-Aided Design)

   For the flexibility of implementing any given Boolean function(s), the FPGA uses re-configurable building blocks called LUTs. The price for this reconfigurability is a large number of registers and multiplexers required to construct the FPGA. While researchers have been working on complex LUT structures to reduce the area and power for several years, most of these implementations come at the cost of performance penalty. This paper demonstrates simultaneous improvement in area, power, and performance in an FPGA by using special logic cells called Threshold Logic Cells (TLCs) (also known as binary perceptrons). The TLCs are capable of implementing a complex threshold function, which if implemented using conventional gates would require several levels of logic gates. The TLCs only require 7 SRAM cells and are significantly faster than the conventional LUTs. The implementation of the proposed FPGA architecture has been done using 28nm FDSOI standard cells and has been evaluated using ISCAS-85, ISCAS-89, and a few large industrial designs. Experiments demonstrate that the proposed architecture can be used to get an average reduction of 18.1% in configuration registers, 18.1% reduction in multiplexer count, 12.3% in Basic Logic Element (BLE) area, 16.3% in BLE power, 5.9% improvement in operating frequency, with a slight reduction in track count, routing area and routing power. The improvements are also demonstrated on the physically designed version of the architecture. 

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4. A Novel Energy-Efficient Sinusoidal Power Clocking-Based Writing Circuitry for the Hybrid CMOS/MTJ Architecture

   https://doi.org/10.1109/TMAG.2024.3430120  

   Yang, Wu; Degada, Amit; Thapliyal, Himanshu (September 2024, IEEE Transactions on Magnetics)

   Spin transfer torque magnetic random access memory (STT-MRAM) offers a promising solution for low-power and high-density memory due to its compatibility with CMOS, higher density, scalable nature, and non-volatility. However, the higher energy required to write bit cells has remained a key challenge for its adaptation into battery-operated smart handheld devices. The existing low-energy writing solutions require additional complex control logic mechanisms, further constraining the available area. In this research, we propose a solution to design energy-efficient write circuits by incorporating two techniques together. First, we propose the sinusoidal power clocking mechanism replacing the DC power supply in the conventional CMOS design. Second, we propose three lookup table (LUT)-based control logic circuits and one write circuit to reduce the area and further minimize energy dissipation. The experimental results are verified over the case study implementations of 4×4 STT-MRAM macro designed using bit cell configurations: i) one transistor and one magnetic tunnel junction (MTJ) (1T-1MTJ) and ii) four transistors and two MTJs (4T-2MTJ). The post-layout simulation for the frequency range from 250 kHz to 6.25 MHz shows that the write circuit, which uses the proposed LUT-based control logic circuits and a write driver with a sinusoidal power supply, achieves more than a 65.05% average energy saving compared to the CMOS counterpart. Furthermore, the write circuit, which uses the proposed 6T write driver with the sinusoidal power supply, shows an improvement in energy saving by more than 70.60% compared to the CMOS counterpart. We also verified that the energy-saving performance remains relatively consistent with the change in temperature and the tunneling magnetoresistance (TMR) ratio. 

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5. MRIMA: An MRAM-based In-Memory Accelerator

   https://doi.org/10.1109/TCAD.2019.2907886  

   Angizi, Shaahin; He, Zhezhi; Awad, Amro; Fan, Deliang (March 2019, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   In this paper, we propose MRIMA, as a novel MRAM-based In-Memory Accelerator for non-volatile, flexible, and efficient in-memory computing. MRIMA transforms current Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) arrays to massively parallel computational units capable of working as both non-volatile memory and in-memory logic. Instead of integrating complex logic units in cost-sensitive memory, MRIMA exploits hardware-friendly bit-line computing methods to implement complete Boolean logic functions between operands within a memory array in a single clock cycle, overcoming the multi-cycle logic issue in contemporary Processing-In-Memory (PIM) platforms. We present practical case studies to demonstrate MRIMA’s acceleration for binary-weight and low bit-width Convolutional Neural Networks (CNN) as well as data encryption. Our device-to-architecture co-simulation results on CNN acceleration demonstrate that MRIMA can obtain 1.7× better energy-efficiency and 11.2× speed-up compared to ASICs, and, 1.8× better energy-efficiency and 2.4× speed-up over the best DRAM-based PIM solutions. As an AES in-memory encryption engine, MRIMA shows 77% and 21% lower energy consumption compared to CMOS-ASIC and recent domain wall-based design, respectively. 

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