TinyML: Current progress, research challenges, and future roadmap
TinyML: Current Progress, Research Challenges, and Future Roadmap Page 1 TinyML:
Current Progress, Research Challenges, and Future Roadmap Muhammad Shafique New …
Current Progress, Research Challenges, and Future Roadmap Muhammad Shafique New …
Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective
G Kornaros - IEEE Access, 2022 - ieeexplore.ieee.org
As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart
devices act in concert, rarely requiring human intervention, posing significant challenges in …
devices act in concert, rarely requiring human intervention, posing significant challenges in …
Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
SIMDRAM: A framework for bit-serial SIMD processing using DRAM
Processing-using-DRAM has been proposed for a limited set of basic operations (ie, logic
operations, addition). However, in order to enable full adoption of processing-using-DRAM …
operations, addition). However, in order to enable full adoption of processing-using-DRAM …
Neural inference at the frontier of energy, space, and time
Computing, since its inception, has been processor-centric, with memory separated from
compute. Inspired by the organic brain and optimized for inorganic silicon, NorthPole is a …
compute. Inspired by the organic brain and optimized for inorganic silicon, NorthPole is a …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
Blockhammer: Preventing rowhammer at low cost by blacklisting rapidly-accessed dram rows
Aggressive memory density scaling causes modern DRAM devices to suffer from
RowHammer, a phenomenon where rapidly activating (ie, hammering) a DRAM row can …
RowHammer, a phenomenon where rapidly activating (ie, hammering) a DRAM row can …
DAMOV: A new methodology and benchmark suite for evaluating data movement bottlenecks
Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
FPGA-based near-memory acceleration of modern data-intensive applications
Modern data-intensive applications demand high computational capabilities with strict
power constraints. Unfortunately, such applications suffer from a significant waste of both …
power constraints. Unfortunately, such applications suffer from a significant waste of both …
Benchmarking a new paradigm: An experimental analysis of a real processing-in-memory architecture
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …