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arxiv: 2605.16213 · v1 · pith:3PC6X2FBnew · submitted 2026-05-15 · 💻 cs.AR

ADS-IMC: Accelerating Data Sorting with In-Memory Computation

Narendra Singh Dhakad , Santosh Kumar Vishvakarma This is my paper

Pith reviewed 2026-05-19 18:18 UTC · model grok-4.3

classification 💻 cs.AR
keywords in-memory computingdata sorting6T SRAMlatency reductionmemory architecturebinary data format
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The pith

In-memory sorting using 6T SRAM cuts data movement costs by keeping operations inside memory.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces architectures that execute sorting directly within the memory fabric using 6T SRAM cells. Conventional sorting requires moving data from memory to a processor and back, which adds substantial latency and energy from the transfers. The new designs avoid those transfers by performing the sort in place. They work with data in the standard weighted binary radix format used in digital systems. The approach reports a 3.4 times lower latency than previous memristor-based in-memory sorting.

Core claim

The paper claims to present the first in-memory sorting architecture built with 6T SRAM. The circuit operates on standard binary radix data and delivers a 3.4x reduction in latency relative to memristor-based IMC sorting.

What carries the argument

A 6T SRAM-based in-memory computation circuit that performs comparisons and rearrangements without moving data outside the memory array.

If this is right

  • Sorting tasks incur lower latency because data stays in memory.
  • Energy costs drop due to the elimination of repeated memory-to-processor transfers.
  • The method applies directly to data already stored in standard binary formats.
  • The architecture supports integration into existing SRAM-based memory structures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar in-memory circuits could support other basic operations such as searching or simple arithmetic.
  • Memory chip designs might evolve to include dedicated support for in-place sorting primitives.
  • Systems that repeatedly sort large datasets could see cumulative efficiency improvements from reduced data movement.

Load-bearing premise

A functional 6T SRAM in-memory sorting circuit can be realized in hardware with the claimed latency benefit and without major area, power, or reliability penalties that offset the gains.

What would settle it

A hardware implementation or detailed simulation of the 6T SRAM sorting circuit that either reaches or falls short of the 3.4x latency reduction while remaining functional.

Figures

Figures reproduced from arXiv: 2605.16213 by Narendra Singh Dhakad, Santosh Kumar Vishvakarma.

Figure 3
Figure 3. Figure 3: Proposed logic design of a 4-bit comparator block [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Logic design of multiplexer block and COPY logic operations directly. By using logic 0 as one of the inputs during the execution of the NOR operation, the NOT logic of the other input is achieved. Similarly, to copy any value, an AND operation is performed using logic 1 as one of the inputs. For sorting two data having 4-bit each, we store data A = A0A1A2A3 in row 3 and data B = B0B1B2B3 is stored in row 4… view at source ↗
Figure 5
Figure 5. Figure 5: Proposed in-memory computing architecture; where Gi [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: illustrates the CAS network used for an eight-input bitonic sorting process. As shown, the network consists of 24 CAS units. Generally, a bitonic sorting network with N inputs requires a specific number of CAS units. These CAS units can be organized into steps or stages, with each stage comprising N/2 CAS units that operate concurrently [16]. Equation 1 and 2 signifies the general expression for the number… view at source ↗
Figure 7
Figure 7. Figure 7: Simulation waveforms of a CAS block, where [PITH_FULL_IMAGE:figures/full_fig_p004_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance comparison data sorting of 4-bit for our proposed design and MemSort [7] [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗
read the original abstract

Sorting is a fundamental operation across numerous computational domains. Traditionally, this process involves transferring data from main memory to a processing unit for sorting, followed by writing the sorted data back to memory. This conventional approach incurs substantial latency and energy overheads due to the extensive data movement between memory and processing components. To mitigate these overheads, this paper introduces novel architectures for executing sorting operations directly within the memory fabric, eliminating the need for off-chip data transfer. To our knowledge, this work represents the first exploration of in-memory sorting using 6T SRAM. The proposed architecture is designed to operate on data represented in the standard weighted binary radix format commonly used in digital systems. The proposed architecture achieves a significant 3.4x reduction in latency compared to memristor-based IMC sorting.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces ADS-IMC, an architecture for in-memory data sorting using standard 6T SRAM cells. It claims to be the first exploration of in-memory sorting with 6T SRAM, operates on data in standard weighted binary radix format, and reports a 3.4x latency reduction relative to prior memristor-based IMC sorting.

Significance. If the latency claim holds with acceptable area/power/reliability overheads, the work would be significant for reducing data-movement costs in a fundamental operation. The use of unmodified 6T SRAM is a strength compared with material-specific approaches. No machine-checked proofs or reproducible artifacts are described.

major comments (2)
  1. [Abstract] Abstract: the 3.4x latency reduction is asserted without any methodology, simulation setup, error analysis, or implementation details, which is load-bearing for the central performance claim.
  2. [Architecture] Architecture section: the description of compare-and-swap steps does not specify the fraction performed via in-array 6T SRAM operations (e.g., bit-line sensing) versus peripheral logic or inter-subarray shuttling; if the latter dominates, the claimed data-movement savings and latency benefit do not follow.
minor comments (1)
  1. [Abstract] Abstract: the novelty statement ('first exploration') would be strengthened by a brief comparison table against the closest prior IMC sorting works.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and will make revisions to improve clarity and substantiation of our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the 3.4x latency reduction is asserted without any methodology, simulation setup, error analysis, or implementation details, which is load-bearing for the central performance claim.

    Authors: We agree that the abstract would benefit from additional context to support the central claim. In the revised version, we will expand the abstract to briefly describe the cycle-accurate simulation framework based on standard 6T SRAM models, the memristor-based IMC baseline used for comparison, and the key assumptions in the latency evaluation. Full details on error analysis and implementation will be retained and cross-referenced in the evaluation section. revision: yes

  2. Referee: [Architecture] Architecture section: the description of compare-and-swap steps does not specify the fraction performed via in-array 6T SRAM operations (e.g., bit-line sensing) versus peripheral logic or inter-subarray shuttling; if the latter dominates, the claimed data-movement savings and latency benefit do not follow.

    Authors: The referee correctly identifies a point that requires clarification. Our design executes the core compare-and-swap logic primarily through in-array 6T SRAM operations using bit-line sensing and word-line activation, with peripheral circuitry limited to control and minimal shuttling between subarrays due to the parallel subarray organization. To address this, we will revise the architecture section to include a quantitative breakdown (e.g., via an added table) of the fraction of latency and operations performed in-array versus any peripheral or shuttling components, thereby substantiating the data-movement reductions. revision: yes

Circularity Check

0 steps flagged

No circularity detected; architecture proposal is self-contained

full rationale

The paper introduces a novel in-memory sorting architecture using 6T SRAM and reports a 3.4x latency improvement over prior memristor IMC work. No equations, derivations, fitted parameters, or self-citations appear in the abstract or described claims. The latency reduction is presented as a direct consequence of the proposed hardware design rather than any mathematical reduction to inputs by construction. The central claim rests on the feasibility of the circuit implementation, which is an external engineering assertion rather than a self-referential loop. This is a standard hardware architecture paper whose derivation chain does not reduce to its own definitions or prior self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based solely on the abstract; no free parameters, axioms, or invented entities are identifiable from the provided text.

pith-pipeline@v0.9.0 · 5659 in / 997 out tokens · 49767 ms · 2026-05-19T18:18:30.777640+00:00 · methodology

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Reference graph

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