Process Variation Resilient Current-Domain Analog In Memory Computing

In-Memory Computing (IMC) has emerged as one of the energy-efficient solutions for data and compute-intensive machine learning applications. Analog IMC architectures have high throughput, but limited bit precision. Process variation further degrades the bit-precision. This work proposes an efficient way to track process variation and compensate for it to achieve high bit-resolution, which, to the best of our knowledge, is first such proposal. PV tracking is achieved by using an additional SRAM column and compensation by a non-conventional word-line driver. The proposed circuit can be augmented to any analog IMC architecture to make it resilient to process variations. To demonstrate the versatility of the proposal, we have implemented and analyzed 2-bit dot product operation in IMC architectures with six different SRAM cell configurations, and 2-bit, 4-bit, and 8-bit dot product on 6T SRAM IMC. For these, we report a reduction of
$4\times$
to
$14\times$
in the standard deviation of statistical variations in bit-line voltage for different SRAM cells, increase in the bit-resolution from 2 bits to 4 bits or 6 bits.