Approach We model the quantization task as a reinforcement learn- ing problem (Figure 2). We use the actor-critic model with DDPG agent to give the action: bits for each layer. We collect hardware counters as constraints, together with ac- curacy as rewards to s...
For a given deep learning task, the peak performance of GPU is often far from the actual performance. In most practical applications, the throughput of a GPU is about 15–20% of its peak performance [111]. It, in turn, implies that evaluating DNNs is actually limited by memory bandwidth,...
Mixed-precision quantization is suggested to adapt to the limited word length. The accuracy evaluation takes stuck-at faults, wire resistances, and resistance variations of the memristors into account. CIM-SIM [15] can also be located at the system level. Contrary to the other tools on system ...
This article thoroughly explores the possibilities and challenges of running LLM on hardware-limited devices like the Raspberry Pi 4B. We proposed using models with smaller memory footprints that can run solely on a CPU and applying model quantization strategies to lower hardware requirements. This ope...
Embodied learning systems, catalyst discovery, drug discovery and protein production optimization are often limited by wet lab labor and cost as well as the lack of convenient computational tools. c, Memristor-based neuromorphic hardware can enables fast and power-efficient DBAL by exploiting device ...
Due to the limited bandwidth of off-chip memory, it has been proved to use the batch mode to increase the data reuse. You can set the BATCH_SIZE in settings.py, the max batch size is 32. It is much better to set appropriate CPF and KPF for each layer to achieve high FPGA resource...
Due to the limited bandwidth of off-chip memory, it has been proved to use the batch mode to increase the data reuse. You can set the BATCH_SIZE in settings.py, the max batch size is 32. It is much better to set appropriate CPF and KPF for each layer to achieve high FPGA resource...
As an example, the ARM device may send an instruction to the processor, such as “start this task”. The instruction is typically multi-cycle. The instruction has all the synchronization signals to load the co-processor (DSP 12 in this case), to take the communication bus, do its ...
For a given deep learning task, the peak performance of GPU is often far from the actual performance. In most practical applications, the throughput of a GPU is about 15–20% of its peak performance [111]. It, in turn, implies that evaluating DNNs is actually limited by memory bandwidth,...
A simulated annealing-based heuristic is proposed in Ref. [57] to determine task mapping that maximizes lifetime reliability. An ant colony optimization approach is proposed in Ref. [58] as an alternative. The approach in Ref. [59] uses Markov decision process to determine the availability of...