Bayesian modeling of quantum-dot-cellular-automata circuits

The goal of this work is to develop a fast, Bayesian Probabilistic Computing model [1],[2] that exploits the induced causality of clocking to arrive at a model with the minimum possible complexity. The probabilities directly model the quantum-mechanical steady-state probabilities (density matrix) or equivalently, the cell polarizations. The attractive feature of this model is that not only does it model the strong dependencies among the cells, but it can be used to compute the steady state cell polarizations, without ..

PIRM: Processing In Racetrack Memories

The growth in data needs of modern applications has created significant challenges for modern systems leading a "memory wall." Spintronic Domain Wall Memory (DWM), related to Spin-Transfer Torque Memory (STT-MRAM), provides near-SRAM read/write performance, energy savings and nonvolatility, potential for extremely high storage density, and does not have significant endurance limitations. However, DWM's benefits cannot address data access latency and throughput limitations of memory bus bandwidth. We propose PIRM, a DWM-based in-memory computing solution that leverages the properties of DWM nanowires and allows them to serve as polymorphic gates. While normally DWM is accessed by applying spin polarized currents orthogonal to the nanowire at access points to read individual bits, transverse access along the DWM nanowire allows the differentiation of the aggregate resistance of multiple bits in the nanowire, akin to a multilevel cell. PIRM leverages this transverse reading to directly provide bulk-bitwise logic of multiple adjacent operands in the nanowire, simultaneously. Based on this in-memory logic, PIRM provides a technique to conduct multi-operand addition and two operand multiplication using transverse access. PIRM provides a 1.6x speedup compared to the leading DRAM PIM technique for query applications that leverage bulk bitwise operations. Compared to the leading PIM technique for DWM, PIRM improves performance by 6.9x, 2.3x and energy by 5.5x, 3.4x for 8-bit addition and multiplication, respectively. For arithmetic heavy benchmarks, PIRM reduces access latency by 2.1x, while decreasing energy consumption by 25.2x for a reasonable 10% area overhead versus non-PIM DWM.Comment: This paper is accepted to the IEEE/ACM Symposium on Microarchitecture, October 2022 under the title "CORUSCANT: Fast Efficient Processing-in-Racetrack Memories

Dependency Preserving Probabilistic Modeling of Switching Activity using Bayesian Networks

We propose a new switching probability model for combinational circuits using a Logic-Induced-Directed-Acyclic-Graph(LIDAG) and prove that such a graph corresponds to a Bayesian Network guaranteed to map all the dependencies inherent in the circuit. This switching activity can be estimated by capturing complex dependencies (spatio-temporal and conditional) among signals efficiently by local message-passing based on the Bayesian networks. Switching activity estimation of ISCAS and MCNC circuits with random input streams yield high accuracy (average mean error=0.002) and low computational time (average time=3.93 seconds)

Integrating Nano-logic into an Undergraduate Logic Design Course

Abstrac