Pith Number

pith:S2K2OBB6

pith:2026:S2K2OBB63UI3TE6DCVBML4F5A2

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ABI: A tightly integrated, unified, sparsity-aware, reconfigurable, compute near-register file/cache GPU architecture with light-weight softmax for deep learning, linear algebra, and Ising compute

Jaydeep P. Kulkarni, Siddhartha Raman Sundara Raman

A tightly integrated near-memory GPU architecture called ABI achieves 6-16 times speedup and 6-13 times energy savings on convolutional neural networks, graph networks, linear programming, large language models, and Ising workloads compared

arxiv:2602.14262 v3 · 2026-02-15 · cs.AR

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\pithnumber{S2K2OBB63UI3TE6DCVBML4F5A2}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We present a tightly integrated and unified near-memory GPU architecture that delivers 6 to 16 times speedup and 6 to 13 times energy savings across Convolutional Neural Networks, Graph Convolutional Networks, Linear Programming, Large Language Models, and Ising workloads compared to MIAOW GPU.

C2weakest assumption

The design assumes that the custom sparsity-aware near-memory circuit and lightweight softmax can be integrated with negligible area, latency, and power overheads while maintaining the claimed scalability across problem sizes and reconfigurability up to INT16.

C3one line summary

ABI is a unified near-memory GPU architecture delivering 6-16x speedup and 6-13x energy savings for deep learning, linear algebra, and Ising compute via sparsity-aware circuits and lightweight softmax.

Formal links

2 machine-checked theorem links

Cited by

4 papers in Pith

Emerging memory technologies at room/cryogenic temperature

A comprehensive study on ILP acceleration accounting for sparsity, area, energy, data movement using near-memory architecture

A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks

A comparative study on power delivery aspects of compute-in/near-memory approaches using DRAM

Receipt and verification

First computed	2026-05-26T02:04:06.350833Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9695a7043edd11b993c31542c5f0bd06b20c663dd35159d1fcb58f8ec38f2bea

Aliases

arxiv: 2602.14262 · arxiv_version: 2602.14262v3 · doi: 10.48550/arxiv.2602.14262 · pith_short_12: S2K2OBB63UI3 · pith_short_16: S2K2OBB63UI3TE6D · pith_short_8: S2K2OBB6

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/S2K2OBB63UI3TE6DCVBML4F5A2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9695a7043edd11b993c31542c5f0bd06b20c663dd35159d1fcb58f8ec38f2bea

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c56b3988998011aeda806936095f40a33220cb5be7bd3610b5be3afca4c738d6",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AR",
    "submitted_at": "2026-02-15T18:19:06Z",
    "title_canon_sha256": "6992e2ef340f701d01efb7fb08348af56d1ec0bca66e691d204e5adccbe19874"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.14262",
    "kind": "arxiv",
    "version": 3
  }
}