hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.
Farah FahimBenjamin HawksChristian HerwigJames HirschauerSergo JindarianiNhan TranLuca P. CarloniGiuseppe Di GuglielmoPhilip C. HarrisJeffrey D. KrupaDylan S. RankinManuel Blanco ValentinJosiah D. HesterYingyi LuoJohn MamishSeda Orgrenci-MemikThea AarrestadHamza JavedVladimir LoncarMaurizio PieriniAdrian Alan PolSioni SummersJavier M. DuarteScott HauckShih-Chieh HsuJennifer NgadiubaMia LiuDuc HoangEdward KreinarZhenbin WuPublished in: CoRR (2021)
Keyphrases
- low power
- open source
- machine learning
- low power consumption
- power consumption
- low cost
- high speed
- maximum likelihood
- embedded systems
- hardware software
- high power
- single chip
- scientific workflows
- digital signal processing
- wireless transmission
- mobile devices
- case study
- source code
- pattern recognition
- signal processor
- real time
- vlsi architecture
- image sensor
- smart phones
- vlsi circuits
- logic circuits
- gate array
- mixed signal
- cmos technology
- rfid tags
- hardware and software