Difference between revisions of "Acceleration and Transprecision"
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Revision as of 08:43, 7 November 2017
Who are we What do we do Where to find us
Available Projects
- Extending our FPU with Internal High-Precision Accumulation (M)
- Low Precision Ara for ML
- Hardware Exploration of Shared-Exponent MiniFloats (M)
- Approximate Matrix Multiplication based Hardware Accelerator to achieve the next 10x in Energy Efficiency: Full System Intregration
- Extended Verification for Ara
- Ibex: Tightly-Coupled Accelerators and ISA Extensions
- RVfplib
- Scalable Heterogeneous L1 Memory Interconnect for Smart Accelerator Coupling in Ultra-Low Power Multicores
Projects In Progress
- Fault-Tolerant Floating-Point Units (M)
- Virtual Memory Ara
- New RVV 1.0 Vector Instructions for Ara
- Big Data Analytics Benchmarks for Ara
- An all Standard-Cell Based Energy Efficient HW Accelerator for DSP and Deep Learning Applications
Completed Projects
- Integrating an Open-Source Double-Precision Floating-Point DivSqrt Unit into CVFPU (1S)
- Investigating the Cost of Special-Case Handling in Low-Precision Floating-Point Dot Product Units (1S)
- Optimizing the Pipeline in our Floating Point Architectures (1S)
- Streaming Integer Extensions for Snitch (M/1-2S)
- A Unified Compute Kernel Library for Snitch (1-2S)
- NVDLA meets PULP
- Hardware Accelerators for Lossless Quantized Deep Neural Networks
- Floating-Point Divide & Square Root Unit for Transprecision
- Low-Energy Cluster-Coupled Vector Coprocessor for Special-Purpose PULP Acceleration
- Design and Implementation of an Approximate Floating Point Unit
