CIDAN: Computing in DRAM with\\Artificial Neurons

TL;DR

CIDAN introduces a novel in-memory architecture using artificial neuron-based processing elements, achieving significant performance and energy efficiency improvements for Boolean function evaluation on large bit vectors.

Contribution

The paper presents CIDAN, a new in-memory computing architecture utilizing threshold logic gates as processing elements, offering substantial performance and energy benefits over existing in-memory architectures.

Findings

3X performance improvement over state-of-the-art in-memory architectures

2X energy efficiency gain in evaluated algorithms

Effective evaluation of Boolean functions on large bit vectors

Abstract

Numerous applications such as graph processing, cryptography, databases, bioinformatics, etc., involve the repeated evaluation of Boolean functions on large bit vectors. In-memory architectures which perform processing in memory (PIM) are tailored for such applications. This paper describes a different architecture for in-memory computation called CIDAN, that achieves a 3X improvement in performance and a 2X improvement in energy for a representative set of algorithms over the state-of-the-art in-memory architectures. CIDAN uses a new basic processing element called a TLPE, which comprises a threshold logic gate (TLG) (a.k.a artificial neuron or perceptron). The implementation of a TLG within a TLPE is equivalent to a multi-input, edge-triggered flipflop that computes a subset of threshold functions of its inputs. The specific threshold function is selected on each cycle by enabling/disabling a subset of the weights associated with the threshold function, by using logic signals. In addition to the TLG, a TLPE realizes some non-threshold functions by a sequence of TLG evaluations. An equivalent CMOS implementation of a TLPE requires a substantially higher area and power. CIDAN has an array of TLPE(s) that is integrated with a DRAM, to allow fast evaluation of any one of its set of functions on large bit vectors. Results of running several common in-memory applications in graph processing and cryptography are presented.