In-Memory Computing With Double Word Lines and Three Read Ports for Four Operands

The von Neumann architecture is approaching its limits in terms of scalability and power consumption. In-memory computation is a possible approach to mitigate this limitation. This brief proposes a configurable 8T static random access memory (SRAM) cell with double word lines and three read ports for in-memory computing. In addition to the normal SRAM function, XOR/XNOR and compound Boolean logic operations of three or four operands, such as AND-OR, AND-OR-INVERT, OR-AND, and OR-AND-INVERT, can be performed in one cycle by fully utilizing the three read ports to obtain 13.2-fJ/bit consumption at 0.6 V. The logic operation frequency is 793 MHz at 1.2 V. The proposed SRAM effectively resolves the bottleneck of the existing in-memory computation schemes that only support compound Boolean logic operations with more than two cycles. In addition, the proposed SRAM array scheme can be configured and used as a binary content-addressable memory or a ternary content-addressable memory for searching operations; it achieves 0.24 fJ/search/bit at 0.6 V in the worst case. At 1.2 V, the searching frequency is up to 813 MHz when searching 128 bits with 65-nm technology.

[1]  Jin-Fu Li,et al.  Configurable 8T SRAM for Enbling in-Memory Computing , 2019, 2019 2nd International Conference on Communication Engineering and Technology (ICCET).

[2]  Kaushik Roy,et al.  8T SRAM Cell as a Multibit Dot-Product Engine for Beyond Von Neumann Computing , 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[3]  David Blaauw,et al.  A 28 nm Configurable Memory (TCAM/BCAM/SRAM) Using Push-Rule 6T Bit Cell Enabling Logic-in-Memory , 2016, IEEE Journal of Solid-State Circuits.

[4]  Kaushik Roy,et al.  X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  David Blaauw,et al.  A 0.3V VDDmin 4+2T SRAM for searching and in-memory computing using 55nm DDC technology , 2017, 2017 Symposium on VLSI Circuits.

[6]  Meng-Fan Chang,et al.  ROBIN: Monolithic-3D SRAM for Enhanced Robustness with In-Memory Computation Support , 2019, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Kaushik Roy,et al.  Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[8]  Kiat Seng Yeo,et al.  Design of a power-efficient CAM using automated background checking scheme for small match line swing , 2013, 2013 Proceedings of the ESSCIRC (ESSCIRC).

[9]  Benton H. Calhoun,et al.  Low-Power Near-Threshold 10T SRAM Bit Cells With Enhanced Data-Independent Read Port Leakage for Array Augmentation in 32-nm CMOS , 2019, IEEE Transactions on Circuits and Systems I: Regular Papers.