Fall 2026

Vlsi Array Structures for Digital Signal Processing

ECE 734 covers VLSI array processor architectures and design methodologies for digital signal processing, focusing on mapping algorithms to array structures.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Fall 2016: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Fall 2018: 3.43 GPA, 42.9% A/AB (n=7 letter grades); Spring 2022: 3.62 GPA, 75.0% A/AB (n=8 letter grades).

Grade history

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average GPA
letter grades
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AB
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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ECE 734

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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Meeting source records

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Student experience

the class

Recent recorded grades — Fall 2016: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Fall 2018: 3.43 GPA, 42.9% A/AB (n=7 letter grades); Spring 2022: 3.62 GPA, 75.0% A/AB (n=8 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • VLSI array processor architectures.
  • Algorithm mapping to array structures.
  • Real-time signal processing applications.

Skills

  • Mapping algorithms onto array structures for real-time signal processing.
  • Understanding VLSI array processor architectures and design methodologies.

Grades

Latest available · Spring 2022— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2022

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Student reviews

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Grade history

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "14029923b7bb9878cd2e3c6ef9481e8fbfba0a3b474948c2fefa659c2554054a",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}