A 28-nm 28.8-TOPS/W Attention-Based NN Processor With Correlative CIM Ring Architecture and Dataflow-Reshaped Digital-Assisted CIM Array
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Bibliographic record
Abstract
Transformer models have achieved impressive performance in various applications by effectively capturing contextual knowledge from the entire sequence. However, the multi-headed self-attention (MHSA) mechanism of Transformer models introduces multiple rounds of matrix multiplication (MM) and Softmax operations, which results in massive data movement and computations. Compute-in-memory (CIM) is a promising candidate to reduce data movement in the memory hierarchy of artificial intelligence (AI) accelerators, increasing the speed and energy efficiency for MM computation. However, the attention mechanism introduces dynamic MMs involving Query (Q), Key (K), and Value (V). Since these matrices are both generated dynamically in previous layers, the dynamic MM mismatches the CIM paradigm, resulting in significant energy/latency consumption. This article proposes a CIM-based transformer accelerator (TranCIM) with three design features, effectively handling dynamic MMs. First, a correlative CIM ring (CRCIMR) executes the dynamic MM that involving Q and K by matrix decomposition, removing the loading of dynamically generated matrix in SRAM-based CIM (SRAM-CIM) cells. Second, a Softmax-based speculation unit (SSU) reduces the computation redundancy in dynamic MMs. Third, a digital-assisted CIM array (DACIMA) executes the dynamic MM that involving V based on symmetrical-L-shaped products, allowing the CIM macro to work in compute mode continuously. Fabricated in a 28-nm CMOS technology, the proposed accelerator occupies an area of 7.08 mm2. Measured on TinyBERT and BERT-Base with INT8 precision, the proposed accelerator achieves a system-level energy efficiency of 28.8 TOPS/W.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it