Search results
- TRIM provides an analytical model to estimate the performance and energy of various DNN hardware archi-tectures. TRIM utilizes a very flexible hardware template, which can model a wide range of architectures.
arxiv.org › pdf › 21051 TRIM: A Design Space Exploration Model for Deep Neural ...
People also ask
What is trim & how does it work?
Is trim a good backup tool?
Does trim work with RAID volumes?
What is the difference between deterministic and non-deterministic trim?
1) TRIM provides an analytical model to estimate the performance and energy of various DNN hardware archi-tectures. TRIM utilizes a very flexible hardware template, which can model a wide range of architectures. TRIM explores the complete space of data partition and reuse strategies for each hardware architecture and estimates the
- Yangjie Qi, Shuo Zhang, Tarek M. Taha
- 2021
In this paper, we present TRIM (TRaining archItecture Model for deep networks), an infrastructure to help hardware architects explore the design space of DNN accelerators for training and inference. It considers both intra-layer workloads and inter-layer workloads of DNNs.
- Yangjie Qi, Shuo Zhang, Tarek M. Taha
- 2021
May 18, 2021 · We present TRIM, an infrastructure to help hardware architects explore the design space of deep neural network accelerators for both inference and training in the early design stages. The model evaluates at the whole network level, considering both inter-layer and intra-layer activities.
- Yangjie Qi, Shuo Zhang, Tarek M. Taha
- 2021
TRIM is an ATA command (Advanced Technology Attachment Command) that allows an OS to inform SSD about the blocks of data no longer in use. The SSD then deletes such blocks of data to make a way for newer blocks of data. TRIM is basically used for enhancing the performance and life span of the SSD.
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting .
Experimental results show that TRIM is a powerful tool for rapidly exploring the design space of DNN architectures for training and inference. Published in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ( Volume: 42 , Issue: 5 , May 2023 )
May 18, 2021 · Experimental results show that TRIM is a powerful tool for rapidly exploring the design space of DNN architectures for training and inference and can quickly explore different hardware design options, select the optimal dataflow and guide new hardware architecture design. Expand. [PDF] Semantic Reader. Save to Library. Create Alert. Cite.
