{ "schema_version": 1, "builder_plan": { "scenario": "Start with one repeated product task that has a measurable failure or cost.", "change": "Read the methods and identify one change you can isolate in that workflow.", "baseline": "Your current implementation on the same held-out tasks.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Successful outcomes, response time, human rework, and total cost per successful task.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2604.23146", "title": "Maximizing Memory-Level Parallelism via Integrated Stochastic Logic-in-Memory Architectures", "summary": "Replaces separate memory and processing in a von Neumann architecture with stochastic arithmetic inside MTJ memory arrays. It requires new hardware not available commercially, accepts precision limits from probabilistic bit streams, and lacks a conventional ML compilation pipeline. 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The sine function is a prominent transcendental function widely used in various applications. Implementing the sine function in hardware presents significant challenges. The 7th order truncated Maclaurin series expansion of 𝐬𝐢𝐧⁡(𝐱) is given by:", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "Sx2.E10", "label": "(10)", "section": "Appendix", "role": "proposed_method", "latex": "sigmoid(x) \\approx\\frac{1}{2}+\\frac{x}{4}-\\frac{x^{3}}{48}+\\frac{x^{5}}{480} \\\\ \\approx 1-\\frac{1}{2}\\left(1-\\frac{x}{2}\\left(1-\\frac{x^{2}}{12}\\left(1-\\frac{x^{2}}{10}\\right)\\right)\\right).", "mathml": "s​i​g​m​o​i​d​(x)≈12+x4−x348+x5480≈1−12​(1−x2​(1−x212​(1−x210))).", "context": "This design also mirrors the structure used for implementing the 𝐬𝐢𝐧⁡(𝐱) function, with the key distinction being the use of different coefficient values. Such a modular representation enables a universal approach to designing transcendental functions within the same architectural framework, requiring only coefficient adjustments tailored to each specific function. ⑤ 𝐬𝐢𝐠𝐦𝐨𝐢𝐝⁡(𝐱). The sigmoid function is a widely used transcendental activation function, particularly prominent in learning systems and neural networks. The 5th order truncated Maclaurin series expansion of the 𝐬𝐢𝐠𝐦𝐨𝐢𝐝⁡(𝐱) function is given by:", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "Sx2.E12", "label": "(12)", "section": "Appendix", "role": "proposed_method", "latex": "e^{-x} =\\sum_{n=0}^{\\infty}\\frac{(-x)^{n}}{n!}\\approx 1-x+\\frac{x^{2}}{2!}-\\frac{x^{3}}{3!}+\\frac{x^{4}}{4!}-\\frac{x^{5}}{5!} \\\\ \\approx 1-x\\left(1-\\frac{x}{2}\\left(1-\\frac{x}{3}\\left(1-\\frac{x}{4}\\left(1-\\frac{x}{5}\\right)\\right)\\right)\\right).", "mathml": "e−x=∑n=0∞(−x)nn!≈1−x+x22!−x33!+x44!−x55!≈1−x⁡(1−x2​(1−x3​(1−x4​(1−x5)))).", "context": "The design of this function closely follows that of the 𝐬𝐢𝐧⁡(𝐱) function, with the primary distinction being the omission of the final stage in the polynomial expansion. ⑦ 𝐞−𝐱. The function 𝐞−𝐱, commonly referred to as the decay function, is a fundamental component in probabilistic modeling and statistical analysis. The 5th order truncated Maclaurin series expansion of the 𝐞​−x function is given by:", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" } ], "equations_status": "selected", "core_kind": "formula", "equations_fetched_at": "2026-09-12", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }