Input-Conscious Approximate Multiply-Accumulate (MAC) Unit for Energy-Efficiency
Bibliographic record
Abstract
The Multiply-Accumulate Unit (MAC) is an integral computational component of all digital signal processing (DSP) architectures and thus has a significant impact on their speed and power dissipation. Due to an extraordinary explosion in the number of battery-powered “Internet of Things” (IoT) devices, the need for reducing the power consumption of DSP architectures has tremendously increased. Approximate computing (AxC) has been proposed as a potential solution for this problem targeting error-resilient applications. In this paper, we present a novel FPGA implementation for input-aware energy-efficient 8-bit approximate MAC (AxMAC) unit that reduces its power consumption by: performing multiplication operation approximately, or approximating the input operands then replacing multiplication by a simple shift operation. We propose an input-aware conditional block to bypass operands multiplication by (1) zero forwarding for zero-value operands, (2) judiciously approximating 43.8% of inputs into power-of-2 values, and (3) replacing the multiplication of power-of-2 operands by a simple shift operation. Experimental results show that these simplification techniques reduce delay, power and energy consumption with an acceptable quality degradation. We evaluate the effectiveness of the proposed AxMAC units on two image processing applications, i.e., image blending and filtering, and a logistic regression classification application. These applications demonstrate a negligible quality loss, with 66.6% energy reduction and 5% area overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".