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Record W1664594238 · doi:10.1109/ccece.2001.933707

Implementation of DSP-RAM: an architecture for parallel digital signal processing in memory

2002· article· en· W1664594238 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceDigital signal processingVHDLComputer hardwareParallel computingSignal processingDiscrete cosine transformComputer architectureField-programmable gate array

Abstract

fetched live from OpenAlex

We describe a synthesizable implementation in VHDL of a parallel architecture for signal processing called DSP-RAM. DSP-RAM is an enhanced version of the earlier computational RAM (C-RAM) architecture proposed by Elliott (see Ph.D. thesis, Dept.of Electrical Engineering, University of Toronto, Canada, 1998). Like C-RAM, the new architecture integrates on the same chip both memory storage and single instruction stream, multiple data stream parallel data processing. Unlike in C-RAM, each processing element contains a multiplier-accumulator that can directly handle 16-bit data words; in contrast, C-RAM is organized to perform massively-parallel, bit-serial computation. The VHDL DSP-RAM model was verified by simulating three promising applications: FIR digital filtering, the discrete cosine transform (DCT), and vector quantization (VQ). A controller circuit along with a simple micro-programming language were also designed to facilitate the implementation of applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.323
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations9
Published2002
Admission routes2
Has abstractyes

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