Paper Citation Record · LEDGER

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine

As of 15 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2506.07046.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.07046 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:48:01.725050Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T04:23:26.079298Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T21:46:42.507564Z

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy26
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37108261-7ae3-47d7-8e60-56700315170c · outbound

This paper cites QuaRL: Quantization for fast and environmentally sustainable reinforcement learning,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine QuaRL: Quantization for fast and environmentally sustainable reinforcement learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.152697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6507f286-bef8-4a96-8524-34fa28d3702b · outbound

This paper cites E2HRL: An energy-efficient hardware ac- celerator for hierarchical deep reinforcement learning,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine E2HRL: An energy-efficient hardware ac- celerator for hierarchical deep reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.140242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd7e719e-6c98-4e6c-8409-0e239e04e3a4 · outbound

This paper cites ChipNEMO: Domain-adapted LLMs for chip design,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine ChipNEMO: Domain-adapted LLMs for chip design,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.127903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7835f54b-0261-4e32-a03e-6ee047150aba · outbound

This paper cites Chip Placement with Deep Reinforcement Learning.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Chip Placement with Deep Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.599158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ade0bb06-5214-492f-8adb-69e95d51c0f6 · outbound

This paper cites Mastering the game of go without human knowledge,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Mastering the game of go without human knowledge,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.115798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b7e2ea6-cdcf-4788-bd61-ad5e83767aa5 · outbound

This paper cites Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.103085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71df8ebb-fd88-408c-bf3d-1a7c798fabd1 · outbound

This paper cites A 2.1TFLOPS/W Mobile Deep RL Accelerator with Transposable PE Array and Experience Compression,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A 2.1TFLOPS/W Mobile Deep RL Accelerator with Transposable PE Array and Experience Compression,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.090023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 74ee6d44-6504-4a87-ba93-e8155e7c48c1 · outbound

This paper cites Explainable Reinforce- ment Learning: A Survey and Comparative Review,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Explainable Reinforce- ment Learning: A Survey and Comparative Review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.075847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 246b4aad-45eb-4bf0-aea5-a3f0009997b4 · outbound

This paper cites Efficient and scalable reinforcement learning for large-scale network control,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Efficient and scalable reinforcement learning for large-scale network control,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.061978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1193ce1-c813-4fd1-a759-4b2a253d3d8c · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.624340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2faea4d1-3aa4-427b-b2f6-ca8891fedf59 · outbound

This paper cites Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.047458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44e07882-eb7f-4908-91ce-49206a4f1166 · outbound

This paper cites Flex-SFU: Activation Function Acceleration with Non-Uniform Piecewise Approximation,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Flex-SFU: Activation Function Acceleration with Non-Uniform Piecewise Approximation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.033649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fd6b7e1a-a967-49f2-9ef1-30f7327895b0 · outbound

This paper cites LPRE: Logarithmic Posit-enabled Reconfigurable edge-AI Engine,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine LPRE: Logarithmic Posit-enabled Reconfigurable edge-AI Engine,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:02.021147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a99cbea8-66e5-4776-95b2-93fca4904462 · outbound

This paper cites A Reconfigurable Processing Element for Multiple- Precision Floating/Fixed-Point HPC,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Reconfigurable Processing Element for Multiple- Precision Floating/Fixed-Point HPC,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.640492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81eea055-2b19-4d60-a0b4-322a72cccb61 · outbound

This paper cites A Configurable Floating-Point Multiple-Precision Processing Element for HPC and AI Converged Computing,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Configurable Floating-Point Multiple-Precision Processing Element for HPC and AI Converged Computing,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.644269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9132cb6-74b6-41ea-b578-fd544a109c1d · outbound

This paper cites High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Ac- celerators,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Ac- celerators,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.993602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a5c06eba-12c6-4c55-8dc5-c2babc8453f6 · outbound

This paper cites QuantMAC: Enhancing Hardware Perfor- mance in DNNs With Quantize Enabled Multiply-Accumulate Unit,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine QuantMAC: Enhancing Hardware Perfor- mance in DNNs With Quantize Enabled Multiply-Accumulate Unit,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.980699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 629185e5-4992-406d-b853-4cfb0b349e7b · outbound

This paper cites Unified Posit/IEEE-754 Vector MAC Unit for Transprecision Computing,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Unified Posit/IEEE-754 Vector MAC Unit for Transprecision Computing,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.656072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d256177-3af0-4663-9079-7effd5cf8dea · outbound

This paper cites A Low-Cost Floating-Point FMA Unit Supporting Package Operations for HPC-AI Applications,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Low-Cost Floating-Point FMA Unit Supporting Package Operations for HPC-AI Applications,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.662167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2551d4ff-1955-4c63-9796-4544340a930f · outbound

This paper cites A Low-Cost Floating-Point Dot-Product-Dual- Accumulate Architecture for HPC-Enabled AI,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Low-Cost Floating-Point Dot-Product-Dual- Accumulate Architecture for HPC-Enabled AI,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.665961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01f6d90c-3c9c-4d4b-9f83-5674bd3290e3 · outbound

This paper cites A Vector Systolic Accelerator for Multi- Precision Floating-Point High-Performance Computing,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Vector Systolic Accelerator for Multi- Precision Floating-Point High-Performance Computing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.945275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa03afe7-fe5a-4acb-8d7c-a88f170d6e40 · outbound

This paper cites Multiple-Mode- Supporting Floating-Point FMA Unit for Deep Learning Processors,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Multiple-Mode- Supporting Floating-Point FMA Unit for Deep Learning Processors,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.932959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dbce18f3-3a63-461c-9927-4852f6a49128 · outbound

This paper cites A Two-Stage Operand Trimming Approximate Logarithmic Multiplier,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Two-Stage Operand Trimming Approximate Logarithmic Multiplier,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.920939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8f5dafe-6571-4f9f-9bc7-a7a1d1c1fce9 · outbound

This paper cites An Empirical Approach to Enhance Performance for Scalable CORDIC-Based Deep Neural Networks,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine An Empirical Approach to Enhance Performance for Scalable CORDIC-Based Deep Neural Networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.908257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c8ea805-c4fe-460f-a6b0-0e0ae0528589 · outbound

This paper cites Efficient CORDIC-Based Activation Functions for RNN Acceleration on FPGAs,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Efficient CORDIC-Based Activation Functions for RNN Acceleration on FPGAs,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.895097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 148ef7ae-48b0-45dd-ad87-b2fdd5224180 · outbound

This paper cites Approximate Softmax Functions for Energy-Efficient Deep Neural Networks,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Approximate Softmax Functions for Energy-Efficient Deep Neural Networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.882984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c9d87d85-6b04-4116-abc4-f85dd43b90bd · outbound

This paper cites A Unified Parallel CORDIC- Based Hardware Architecture for LSTM Network Acceleration,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Unified Parallel CORDIC- Based Hardware Architecture for LSTM Network Acceleration,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.869718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0d737602-ec64-49db-b89b-11a37e75f96b · outbound

This paper cites Synergy: An HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Synergy: An HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.856018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87d0b813-fbb3-4266-b086-006b005103e5 · outbound

This paper cites Real-Time SSDLite Object Detection on FPGA,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Real-Time SSDLite Object Detection on FPGA,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.843475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a331e366-71b1-42df-85aa-a669bde83257 · outbound

This paper cites ShortcutFusion: From Tensorflow to FPGA-Based Accelerator With a Reuse-Aware Memory Allocation for Shortcut Data,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine ShortcutFusion: From Tensorflow to FPGA-Based Accelerator With a Reuse-Aware Memory Allocation for Shortcut Data,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.830684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 36ad0d91-b777-4c2f-b1d4-c0f25e592fea · outbound

This paper cites A High-Throughput Full-Dataflow Mo- bileNetv2 Accelerator on Edge FPGA,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A High-Throughput Full-Dataflow Mo- bileNetv2 Accelerator on Edge FPGA,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.818366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce62761c-4321-4d96-b17a-c46a9a4ee973 · outbound

This paper cites A Real-Time Object Detection Processor With xnor-Based Variable-Precision Computing Unit,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine A Real-Time Object Detection Processor With xnor-Based Variable-Precision Computing Unit,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.804674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 014935f4-98b4-4e1f-ae5f-ffbec7b70a0d · outbound

This paper cites Edge-Side Fine-Grained Sparse CNN Accelerator With Efficient Dynamic Pruning Scheme,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Edge-Side Fine-Grained Sparse CNN Accelerator With Efficient Dynamic Pruning Scheme,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:01.720546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9c13608-a3b8-40f8-990b-3c48b9f02628 · outbound

This paper cites Low Latency Hybrid CORDIC Algorithm,.

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine Low Latency Hybrid CORDIC Algorithm,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:01.784249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 2f21c3fe-b503-4270-a5ad-79493d9909d5 · inbound

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment cites this paper.

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:42.510574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.