In-memory Multi-valued Associative Processor
Mira Hout, Mohammed E. Fouda, Rouwaida Kanj, and Ahmed M. Eltawil

TL;DR
This paper introduces a novel in-memory associative processor architecture extended to multi-valued logic, specifically ternary, enabling efficient in-memory arithmetic operations with significant improvements in energy, delay, and area.
Contribution
It presents a structured methodology for automatic LUT generation for multi-valued in-memory computing and introduces a ternary associative processor architecture with optimized in-place addition.
Findings
Ternary AP adder reduces energy by 12.25% and area by 6.2% compared to binary AP adder.
Ternary AP demonstrates 52.64% energy reduction over binary, and up to 9.5x faster delay than state-of-the-art ternary carry-lookahead adder.
Proposed methodologies effectively enable in-memory multi-valued arithmetic operations.
Abstract
In-memory associative processor architectures are offered as a great candidate to overcome memory-wall bottleneck and to enable vector/parallel arithmetic operations. In this paper, we extend the functionality of the associative processor to multi-valued arithmetic. To allow for in-memory compute implementation of arithmetic or logic functions, we propose a structured methodology enabling the automatic generation of the corresponding look-up tables (LUTs). We propose two approaches to build the LUTs: a first approach that formalizes the intuition behind LUT pass ordering and a more optimized approach that reduces the number of required write cycles. To demonstrate these methodologies, we present a novel ternary associative processor (TAP) architecture that is employed to implement efficient ternary vector in-place addition. A SPICE-MATLAB co-simulator is implemented to test the…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Parallel Computing and Optimization Techniques
