X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories

Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-art computing platforms. Despite tremendous strides in scaling the ubiquitous metal-oxide-semiconductor transistor, the underlying $\mathit{\text{von-Neumann}}$ computing architecture has remained unchanged. The limited throughput and energy-efficiency of the state-of-art computing systems, to a large extent, results from the well-known $\mathit{\text{von-Neumann}}\mathit{\text{bottleneck}}$. The energy and throughput inefficiency of the von-Neumann machines have been accentuated in recent times due to the present emphasis on data-intensive applications like artificial intelligence, machine learning $\mathit{\text{etc}}$. A possible approach towards mitigating the overhead associated with the von-Neumann bottleneck is to enable $\mathit{\text{in-memory}}$ Boolean computations. In this manuscript, we present an augmented version of the conventional SRAM bit-cells, called $\mathit{\text{the}}\mathit{\text{X-SRAM}}$, with the ability to perform in-memory, vector Boolean computations, in addition to the usual memory storage operations. We propose at least six different schemes for enabling in-memory vector computations including NAND, NOR, IMP (implication), XOR logic gates with respect to different bit-cell topologies $-$ the 8T cell and the 8${}^{+}$T Differential cell. In addition, we also present a novel $\mathit{\text{‘read-compute-store’}}$ scheme, wherein the computed Boolean function can be directly stored in the memory without the need of latching the data and carrying out a subsequent write operation. The feasibility of the proposed schemes has been verified using predictive transistor models and Monte-Carlo variation analysis.