In this work, we present a novel approach to real-time tracking of full-chip heatmaps for commercial off-the-shelf microprocessors based on machine-learning. The proposed post-silicon approach, named RealMaps, only uses the existing embedded temperature
sensors and workload-independent utilization information, which are available in real-time. Moreover, RealMaps does not require any knowledge of the proprietary design details or manufacturing process-specific information of the chip. Consequently, the methods presented in this work can be implemented by either the original chip manufacturer or a third party alike, and is aimed at supplementing, rather than substituting, the temperature data sensed from the existing embedded sensors. The new approach starts with offline acquisition of accurate spatial and temporal heatmaps using an infrared thermal imaging setup while nominal working conditions are maintained on the chip. To build the dynamic thermal model, a temporal-aware long-short-term-memory (LSTM) neutral network is trained with system-level features such as chip frequency, instruction counts, and other high-level performance metrics as inputs. Instead of a pixel-wise heatmap estimation, we perform 2D spatial discrete cosine transformation (DCT) on the heatmaps so that they can be expressed with just a few dominant DCT coefficients. This allows for the model to be built to estimate just the dominant spatial features of the 2D heatmaps, rather than the entire heatmap images, making it significantly more efficient. Experimental results from two commercial chips show that RealMaps can estimate the full-chip heatmaps with 0.9C and 1.2C root-mean-square-error respectively and take only 0.4ms for each inference which suits well for real-time use. Compared to the state of the art pre-silicon approach, RealMaps shows similar accuracy, but with much less computational cost.
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Chunks are not "Content-Free": Hierarchical Representations Preserve Perceptual Detail within Chunks.
Chunks allow us to use long-term knowledge to efficiently
represent the world in working memory. Most views of
chunking assume that when we use chunks, this results in the
loss of specific perceptual details, since it is presumed the
contents of chunks are decoded from long-term memory rather
than reflecting the exact details of the item that was presented.
However, in two experiments, we find that in situations where
participants make use of chunks to improve visual working
memory, access to instance-specific perceptual detail (that
cannot be retrieved from long-term memory) increased, rather
than decreased. This supports an alternative view: that chunks
facilitate the encoding and retention into memory of perceptual
details as part of structured, hierarchical memories, rather than
serving as mere “content-free” pointers. It also provides a
strong contrast to accounts in which working memory capacity
is assumed to be exhaustively described by the number of
chunks remembered.
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- Award ID(s):
- 1829434
- NSF-PAR ID:
- 10297810
- Date Published:
- Journal Name:
- Proceedings of the Annual Conference of the Cognitive Science Society
- Volume:
- 43
- ISSN:
- 1069-7977
- Page Range / eLocation ID:
- 721-727
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process,
chunking , may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word‐like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test–retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two‐alternative forced‐choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature. -
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
